High-Level Synthesis

High-Level Synthesis (HLS) is an automated design methodology that translates behavioral descriptions of hardware written in high-level programming languages into production-quality Register-Transfer Level (RTL) implementations, such as Verilog, VHDL, or SystemVerilog [1][2]. This process abstracts away the complexities of manual RTL coding, enabling designers and developers to focus on algorithmic optimization and system-level architecture rather than low-level hardware details [1][3]. As a synthesis technology, HLS transforms an untimed behavioral specification into a timed RTL design, performing critical optimizations like scheduling operations across clock cycles and sharing hardware resources to improve the power, performance, and area (PPA) of the resulting circuit [6]. The methodology fundamentally bridges the software and hardware design domains, allowing specifications traditionally used for software—such as C, C++, OpenCL, or SystemC—to be synthesized into hardware suitable for implementation on Field-Programmable Gate Arrays (FPGAs) or Application-Specific Integrated Circuits (ASICs) [2][3][1]. The core characteristic of HLS is its use of source code written in high-level languages that is specifically structured and annotated for automated translation into hardware [4]. This HLS code embodies hardware-specific constraints, explicitly exposes parallelism, and leverages optimization directives or pragmas to communicate architectural intent to the synthesis tools [4]. The synthesis process itself involves several key steps: the high-level specification is compiled, scheduled (assigning operations to clock cycles), allocated (selecting hardware resources like adders and multipliers), and bound (mapping operations to those resources), before finally generating the RTL description [6]. Main types or approaches within HLS can be understood through the languages used (e.g., C-based vs. SystemC-based flows) and the target implementation platform, primarily FPGA or ASIC [2][1]. Commercial and academic tools, such as AMD's Vitis HLS and Siemens' Catapult, operationalize this flow by synthesizing C/C++ functions into RTL and integrating with downstream implementation and verification toolchains [5][1]. The primary application of High-Level Synthesis is in the design of digital hardware for FPGAs and ASICs, significantly accelerating the design process for complex algorithms in domains like digital [signal processing](/page/signal-processing "Signal processing is a fundamental engineering discipline..."), machine learning acceleration, computer vision, and wireless communications [3][5]. Its significance lies in raising the level of design abstraction, which improves designer productivity, facilitates architectural exploration, and enhances the verification of hardware systems [1][1]. By allowing software engineers to contribute effectively to hardware design and enabling hardware engineers to iterate more rapidly on algorithmic implementations, HLS has become a cornerstone of modern electronic design automation (EDA). Its modern relevance is underscored by ongoing research into advanced optimization techniques, including the use of machine learning for tasks like dependency-aware scheduling, to further improve the quality of results [1]. The methodology represents a pivotal shift in hardware design, connecting algorithmic development directly to efficient hardware realization.

Overview

High-Level Synthesis (HLS) is an automated design process that transforms algorithmic specifications written in high-level programming languages into production-quality Register-Transfer Level (RTL) hardware descriptions suitable for implementation on Field-Programmable Gate Arrays (FPGAs) or Application-Specific Integrated Circuits (ASICs) [10]. This methodology fundamentally bridges the traditional gap between software and hardware engineering domains, allowing designers to express system functionality using languages like C, C++, SystemC, or OpenCL, which are then automatically compiled into equivalent hardware structures described in RTL languages such as Verilog or VHDL [11]. The primary objective of HLS is to elevate the abstraction level for hardware design, enabling a focus on algorithmic behavior, data flow, and system-level optimization rather than the intricate, cycle-accurate details of finite state machines, datapath scheduling, and resource binding that characterize manual RTL design [10].

Core Process and Abstraction Shift

The HLS workflow involves several critical, automated stages that translate sequential, untimed software code into parallel, timed hardware. The process typically begins with the designer providing a high-level specification, which is often a subset of C/C++ augmented with compiler directives or pragmas that guide the synthesis process [11]. The HLS tool first performs dependency analysis on the input code, constructing a data flow graph (DFG) and a control flow graph (CFG) to understand the inherent parallelism and sequential constraints of the algorithm [11]. Following this analysis, the scheduling phase assigns each operation in the algorithm to specific clock cycles, a complex optimization problem that balances performance (latency), resource utilization (area), and power consumption [11]. For example, an operation like c = a + b must be scheduled in a cycle where its operands are available, and the result is stored for subsequent use. Subsequently, the binding (or allocation) phase maps the scheduled operations to specific hardware resources from a target library, such as deciding whether multiple addition operations will share a single physical adder (resource sharing) or be implemented with dedicated units [10]. This is followed by controller generation, where the tool synthesizes a finite state machine (FSM) that orchestrates the sequence of operations, manages control signals like multiplexer selects, and handles data movement between registers and functional units [11]. The final output is a cycle-accurate RTL description that includes a datapath (registers, functional units, multiplexers, and interconnects) and a controller, fully synthesizable for downstream logic synthesis and place-and-route tools [10].

Key Advantages and Design Impact

The adoption of HLS offers significant advantages over traditional manual RTL design methodologies. Foremost is a dramatic increase in design productivity, as designers can work at a higher level of abstraction, describing intent and functionality more concisely. A single line of high-level code can represent complex operations that would require dozens of lines of RTL, reducing development time and the potential for human error [10]. This abstraction also facilitates rapid design space exploration. Designers can quickly evaluate different micro-architectural trade-offs—such as trading parallelism for area or pipelining for throughput—by modifying high-level constraints or directives and re-running synthesis, rather than undertaking time-consuming manual RTL rewrites [10]. Furthermore, HLS enables functional verification at a higher level. The input C/C++ specification can be validated using mature software testing frameworks, simulation environments, and debuggers long before RTL is generated. This "shift-left" in verification allows for the early detection of algorithmic bugs [10]. Once the high-level model is verified, the generated RTL can be validated for functional equivalence using formal or simulation-based techniques, streamlining the overall verification flow [10]. From a quality-of-results (QoR) perspective, modern HLS tools are engineered to produce implementations that are competitive with hand-coded RTL in terms of performance, power consumption, and silicon area, while providing the aforementioned productivity benefits [10].

Technical Challenges and Modern Solutions

Despite its advantages, HLS presents unique technical challenges. The quality of the final RTL is heavily dependent on the sophistication of the underlying scheduling and binding algorithms. These are NP-hard optimization problems that must consider complex constraints and objectives simultaneously [11]. For instance, scheduling must account for:

Operation dependencies (data, control, and memory)
Resource constraints (limited numbers of adder, multiplier, or memory ports)
Timing constraints (target clock frequency and overall latency)
Power constraints (minimizing switching activity and dynamic power)

Traditional heuristic-based schedulers, such as list scheduling or force-directed scheduling, may not find optimal solutions for complex, non-linear code structures [11]. Recent research addresses these challenges with advanced techniques. For example, one approach formulates scheduling as a reinforcement learning problem guided by a Graph Neural Network (GNN) [11]. In this method, the HLS tool's intermediate representation (the DFG/CFG) is modeled as a graph. A GNN processes this graph to learn rich representations of operations and their dependencies. A reinforcement learning agent then uses these representations to make sequential scheduling decisions, learning a policy that maximizes a reward function encoding the designer's objectives (e.g., Reward = - (α * Latency + β * Area)). This data-driven approach can discover scheduling strategies that outperform classical heuristics, particularly for irregular code structures [11]. Another persistent challenge is the semantic gap between software languages and hardware realities. Software languages like C/C++ assume a large, flat memory address space and sequential execution, whereas hardware implementations involve distributed register files, block RAMs with limited ports, and explicit parallelism. Effective HLS requires the designer to write "hardware-aware" code or use tool-specific pragmas to guide the translation—for instance, specifying array partitioning into multiple RAMs to increase memory bandwidth or indicating which loops should be pipelined or unrolled [10].

Applications and Industry Adoption

High-Level Synthesis has found substantial adoption in several domains where algorithmic complexity meets the need for hardware acceleration. In digital signal processing (DSP) and wireless communications, algorithms for 5G beamforming, image processing filters, and video codecs (like H.264/HEVC encoding) are often prototyped and implemented using HLS due to the ease of translating mathematical operations into hardware [10]. The financial technology sector uses HLS to accelerate complex option pricing models and risk analysis algorithms on FPGAs in low-latency trading systems. Furthermore, the rise of machine learning acceleration has made HLS particularly valuable for implementing custom neural network layers, activation functions, and specialized tensor operations that may not map efficiently to generic GPUs or existing ASIC cores [11]. Industry platforms, such as Siemens' Catapult HLS, exemplify the commercial maturation of this technology. These tools accept standard C++ or SystemC and integrate verification (HLV) solutions to ensure the generated RTL is functionally correct and meets performance, power, and area (PPA) targets that are competitive with manual design [10]. The ongoing evolution of HLS is increasingly intertwined with machine learning, not only as a target application but also as a methodology to enhance the synthesis algorithms themselves, promising continued improvements in the automation and quality of hardware design from high-level specifications [11].

History

The development of High-Level Synthesis (HLS) represents a significant evolution in electronic design automation (EDA), driven by the growing complexity of digital systems and the widening productivity gap between software and hardware design. Its history is characterized by a transition from early academic concepts focused on formal synthesis to the emergence of commercial tools that translate high-level programming languages into hardware descriptions.

Early Academic Foundations (1970s–1980s)

The conceptual origins of HLS can be traced to the 1970s and early 1980s, rooted in research on hardware description languages (HDLs) and the automation of digital system design. Early work was often termed "behavioral synthesis" and focused on translating algorithmic descriptions into structural implementations. Pioneering research at institutions like Carnegie Mellon University and the University of California, Berkeley, explored the synthesis of digital circuits from abstract specifications. These early systems, such as the CMU-DA (Design Automation) project, dealt with synthesis from a register-transfer level (RTL) or algorithmic state machine (ASM) level, which was still closer to hardware than the software-oriented languages used in modern HLS [5]. A key challenge was the "scheduling" problem—determining which operations occur in which clock cycles—and the "binding" problem—mapping those operations to specific hardware resources like functional units and registers. Foundational algorithms for list scheduling, force-directed scheduling, and resource sharing were developed during this period, establishing the core computational problems that HLS tools must solve [5].

The Rise of Commercial Exploration (Late 1980s–1990s)

By the late 1980s and 1990s, academic research began transitioning to commercial ventures. Early commercial tools, such as those from Synopsys (Behavioral Compiler) and Cadence (Cierto VCC), emerged. These tools accepted behavioral descriptions in HDLs like VHDL or Verilog, which were more abstract than RTL but still required hardware design expertise. They aimed to automate the transformation from a clock-cycle-agnostic behavioral model to a cycle-accurate RTL implementation. However, adoption was limited by several factors:

The input languages (VHDL/Verilog behavioral) were not widely used by software engineers. - The quality of results (QoR) often lagged behind manually crafted RTL in terms of performance and area. - The tools required extensive directives and constraints to guide the synthesis process effectively. Despite these challenges, this era validated the core premise of synthesis above the RTL and set the stage for a more significant shift: targeting languages familiar to software and algorithm developers [5].

The C-Based Synthesis Era (2000s–2010s)

A major turning point occurred in the 2000s with the introduction of HLS tools that accepted subsets of C, C++, and later SystemC as input languages. This shift was driven by the need to bridge the gap between system architects, who modeled algorithms in C/C++, and hardware implementation teams. Companies like Mentor Graphics (Catapult C), Forte Design Systems (Cynthesizer), and later Xilinx (AutoESL, which became Vivado HLS) and Cadence (Stratus HLS) pioneered this approach. The fundamental promise was to allow designers to describe functionality at the algorithmic level and automatically generate efficient RTL. This process involved several sophisticated steps applied to the high-level code:

Scheduling: Assigning operations to specific clock cycles, often employing techniques like pipelining to improve throughput. For example, a loop with a loop-carried dependency might be scheduled over multiple cycles, while an independent loop could be partially unrolled and parallelized.
Resource Allocation and Binding: Determining the number and type of hardware units (e.g., adders, multipliers) and mapping operations to them.
Interface Synthesis: Generating hardware interface protocols (e.g., AXI4, Avalon) to communicate with other system blocks, transforming software-like function arguments into hardware ports with specific handshaking protocols [5]. A critical development was the focus on architecture exploration. Designers could rapidly evaluate different micro-architectures by applying directives (e.g., loop unrolling, pipelining, array partitioning) to the same C source, enabling trade-off analysis between latency, throughput, and resource utilization without rewriting RTL code [10].

Mainstream Adoption and Ecosystem Expansion (2010s–Present)

The 2010s saw HLS move toward mainstream adoption, particularly in FPGA-based design and acceleration. Key drivers included the increasing use of FPGAs for heterogeneous computing, the rise of domain-specific architectures, and the need for rapid prototyping of complex algorithms. Major FPGA vendors, AMD (formerly Xilinx) with Vitis HLS and Intel with its HLS compiler, integrated C-based synthesis deeply into their toolflows. The ecosystem expanded to include higher-level entry points. For instance, algorithms developed in MATLAB® could be synthesized into AMD Vitis™ HLS-compatible C++ code via MathWorks HDL Coder, expediting the translation from a high-level algorithmic design directly to a low-level RTL implementation [5]. This workflow connected model-based design environments directly to hardware synthesis. Furthermore, the advent of OpenCL and high-level frameworks for FPGA programming, while sometimes distinct from traditional HLS, shared the goal of abstracting hardware details and contributed to the broader acceptance of high-level hardware design methodologies. The focus expanded from single-block synthesis to system-level integration, where HLS-generated IP blocks with standard interfaces (like AXI4) could be connected in system-on-chip (SoC) architectures containing processors and other peripherals [5][10].

Current State and Future Trajectory

Today, HLS is an established methodology within the EDA landscape. Its primary value proposition remains accelerating design productivity and enabling extensive architectural exploration early in the design cycle. As noted in industry sources, HLS allows teams to answer critical questions upfront: "Will your hardware be system performance limited? Did you pick the right fundamental memory architecture?" without waiting for full RTL implementation and system integration [10]. Modern tools incorporate increasingly sophisticated optimization engines for tasks like automatic loop pipelining, dataflow optimization, and intelligent resource sharing. The methodology is particularly dominant in areas like digital signal processing, where its ability to efficiently implement complex, dataflow-oriented algorithms is paramount. The historical evolution of HLS—from behavioral synthesis of HDLs to the synthesis of software languages—reflects the ongoing effort to raise the abstraction level in hardware design, making it more accessible and efficient in an era of extreme system complexity. Future directions likely involve tighter integration with machine learning for optimization, support for more dynamic language features, and deeper co-design with high-level software frameworks.

This process bridges the traditional gap between software and hardware engineering, allowing developers to describe functionality using languages like C, C++, SystemC, or OpenCL, which are then automatically synthesized into equivalent Verilog or VHDL code [12][12]. The primary objective of HLS is to elevate the abstraction level for hardware design, enabling a focus on algorithmic behavior, architectural exploration, and functional validation before committing to low-level RTL implementation, thereby significantly accelerating the design cycle [12].

Core Synthesis Process and Translation

The HLS workflow fundamentally converts untimed, sequential software descriptions into timed, concurrent hardware architectures. The process begins with the HLS tool analyzing the high-level source code, which is typically written with hardware implementation constraints in mind [12]. A key initial step involves interface synthesis, where the tool adds hardware interface protocols to the untimed C++ or C design, defining how the resulting hardware block will communicate with the rest of the system through ports for data, control, and clocks [12]. Following this, the core synthesis tasks are performed:

Scheduling: The tool determines which operations occur during which clock cycles. It analyzes data dependencies and resource constraints to create a state machine that controls the sequence of operations [12].
Resource Allocation and Binding: The scheduler works in tandem with a resource allocator. The allocator selects which hardware components (e.g., adders, multipliers, memory blocks) will be used from a target library, and the binder maps specific operations in the code to these physical resources [12].
RTL Generation: Finally, the tool generates the detailed RTL description, which includes a datapath comprised of the allocated components, a control unit (finite state machine) derived from the schedule, and the synthesized interfaces [12]. A central mechanism in this translation is that C++ classes or functions are synthesized as concurrent, clocked processes in the resulting RTL [12]. This means a function intended for hardware acceleration becomes a dedicated hardware module that operates in parallel with other system components, unlike its sequential execution in software.

Characteristics of HLS Code

Code intended for HLS possesses distinct characteristics that differentiate it from general-purpose software, reflecting the constraints and opportunities of hardware implementation.

Static Memory and Bounded Loops: Dynamic memory management (e.g., malloc, free) is generally not synthesizable. Instead, all data structures, particularly arrays, must have static, compile-time constant sizes to enable the tool to infer fixed hardware resources like Block RAMs (BRAMs) or UltraRAMs (URAMs) [12]. Similarly, loop bounds must typically be determinable at synthesis time to allow for precise resource planning and scheduling [12].
Explicit Parallelism and Pipelining: While software is inherently sequential, hardware excels at parallelism. HLS code is often structured and annotated to expose parallelism. Key techniques include:
Loop Unrolling: Replicating loop body logic to perform multiple iterations concurrently. This is controlled by pragmas like #pragma HLS UNROLL factor=<f> [12].
Pipelining: Overlapping the execution of successive loop iterations or operations to increase throughput. The initiation interval (II), or the number of clock cycles between starting new inputs, is a critical performance metric specified by directives such as #pragma HLS PIPELINE II=<ii> [12].
Dataflow: Enabling concurrent execution of multiple functions or tasks by using channels or streams for data communication, activated by #pragma HLS DATAFLOW [12].

Optimization Directives and Pragmas

HLS tools provide a set of compiler directives, often implemented as pragmas in the source code, to guide the synthesis process and optimize the resulting hardware. These pragmas allow the designer to control aspects that are ambiguous in pure C/C++ [12][12].

Interface Specification: Directives define whether function arguments become input/output ports, memory interfaces (e.g., AXI4 streams or memory-mapped interfaces), or internal registers [12].
Memory Architecture Optimization: Arrays in C code are mapped to specific hardware memory resources. Pragmas like #pragma HLS ARRAY_PARTITION can split a large array into smaller, independently accessible units to increase memory bandwidth and enable parallel access [12]. Conversely, #pragma HLS ARRAY_RESHAPE can combine elements to optimize the data path width.
Resource Binding: Designers can specify exactly which hardware primitive (e.g., a DSP48 slice or a specific type of RAM core) should be used for a given operation or variable using directives like #pragma HLS RESOURCE variable=buf core=RAM_2P [12].
Dependency Management: To enable more aggressive scheduling and pipelining, designers can inform the tool about false or assumed data dependencies using pragmas like #pragma HLS DEPENDENCE variable=arr inter false [12].

Verification and Tool Flows

A significant advantage of the HLS methodology is the ability to verify functionality at a higher abstraction level and earlier in the design cycle. Modern HLS tools integrate comprehensive verification flows [11].

C Simulation: The pure C/C++ algorithm is compiled and executed using a standard software compiler and a C test bench. This step is fast and is used for initial algorithmic validation and debugging [11].
C/RTL Co-Simulation: After RTL is generated, the HLS tool automatically reuses the original C test bench to drive a cycle-accurate simulation of the generated RTL (Verilog/VHDL). This verifies that the RTL behavior matches the C source code. The flow typically includes integrated waveform viewers and supports industry-standard logic simulators [11].

Commercial HLS Tools and Ecosystems

The HLS landscape is supported by several major commercial tools, each integrated into broader hardware design ecosystems [12][12].

AMD Vitis™ HLS: The primary HLS tool within the AMD Vitis™ unified software platform, targeted at AMD (formerly Xilinx) FPGAs and adaptive SoCs. It accepts C, C++, and OpenCL as input and is tightly integrated with device-specific architectures for optimization. Much of its foundational technology and documentation originated from its predecessor, Vivado HLS [12][12].
Intel HLS Compiler: A component of the Intel® Quartus® Prime design software, focused on synthesizing C++ and OpenCL designs for Intel FPGAs. It emphasizes optimizations for Intel's specific FPGA architectures [12].
Cadence Stratus HLS: A high-level synthesis solution supporting C, C++, and SystemC, targeting both ASIC and FPGA implementations. It is part of the broader Cadence verification and implementation suite [12].
Siemens EDA Catapult HLS: A platform-independent HLS tool that also supports C++ and SystemC for ASIC and FPGA targets. It provides features like power estimation and optimization for ASIC flows [12][10]. These tools embody the industry shift towards design at a higher level of abstraction, building on the foundation of C-based synthesis established in earlier decades. Their integrated flows for synthesis, optimization, and verification enable the practical application of HLS across various domains, from embedded systems to high-performance computing.

Significance

High-Level Synthesis represents a paradigm shift in digital hardware design, fundamentally altering the relationship between software algorithms and physical hardware implementation. Its significance extends beyond mere productivity gains, enabling new methodologies, fostering interdisciplinary collaboration, and creating ecosystems that bridge traditionally separate domains. The process automates the generation of production-quality Register-Transfer Level (RTL) implementations from algorithmic specifications written in high-level languages, connecting software design directly to hardware realization [13].

Bridging Algorithmic Design and Hardware Implementation

The core significance of HLS lies in its ability to translate abstract algorithmic descriptions into efficient hardware structures. This translation involves several automated steps: analyzing high-level code, partitioning algorithms, scheduling operations, allocating resources, and finally generating equivalent RTL descriptions [13]. Unlike traditional RTL design where engineers manually describe clock-cycle accurate behavior, HLS allows developers to focus on algorithmic correctness and performance characteristics while the tool handles low-level implementation details. This abstraction enables extensive architectural exploration early in the design cycle, as different implementation strategies can be rapidly evaluated without rewriting fundamental code. The synthesis process transforms different elements of C/C++ code systematically. Top-level function arguments become RTL I/O ports with automatically synthesized interface protocols, while other functions maintain design hierarchy as distinct RTL blocks [13]. Control structures and data operations are mapped to hardware equivalents, with loops either kept rolled for sequential execution or transformed through optimization techniques. Arrays in the source code can be targeted to specific memory resources like Block RAM (BRAM), Look-Up Table RAM (LUTRAM), or UltraRAM (URAM) based on performance and resource requirements [13]. This automated mapping from software constructs to hardware resources represents a fundamental advancement in design methodology.

Optimization Frameworks and Performance Transformation

HLS tools employ sophisticated optimization techniques that unlock the full potential of custom hardware implementations. These optimizations operate at multiple levels, transforming sequential algorithms into parallel hardware architectures. Loop optimizations are particularly significant, with loop unrolling increasing parallelism by replicating loop bodies to execute multiple iterations concurrently, and loop pipelining allowing new iterations to begin before previous ones complete, thereby improving overall throughput [1]. Dataflow optimization focuses on maximizing data movement between operations through strategic task scheduling and memory access patterns, creating efficient processing pipelines [1]. The Vitis HLS tool exemplifies these capabilities through specific parallel programming constructs. HLS tasks enable process-level concurrency, allowing independent functions to execute simultaneously. HLS vectors provide data-level parallelism through Single Instruction Multiple Data (SIMD) operations, while HLS streams facilitate communication between concurrent tasks through First-In-First-Out (FIFO) buffers [13]. These constructs are controlled through synthesis pragmas—directives embedded in the source code that guide the translation process. Key pragmas include pipeline (controlling initiation intervals), unroll (specifying replication factors), array partitioning (distributing arrays across memory banks), and interface protocols (defining communication standards) [13]. Through these mechanisms, HLS transforms sequential algorithms into highly parallel hardware implementations that can achieve performance levels difficult to attain through manual RTL design.

Expanding Input Language Ecosystems

While C and C++ remain predominant input languages, the HLS ecosystem has expanded to incorporate diverse specification methods, each serving particular application domains. Python has emerged as a viable input language through various frameworks, leveraging its popularity in scientific computing and machine learning. More specialized domain-specific languages have also been integrated, most notably P4 (Programming Protocol-Independent Packet Processors), a language designed specifically for specifying packet processing algorithms in networking hardware such as switches and routers. This expansion beyond traditional C-based inputs demonstrates HLS's adaptability to different problem domains and specification styles. MATLAB® represents another significant input ecosystem, where functions developed in this computational environment can be synthesized into HLS-compatible C++ code via MathWorks HDL Coder. Available since MATLAB R2025a, this code generation capability produces synthesizable C++ that serves as direct input to tools like AMD Vitis™ HLS. This workflow significantly expedites the translation process from mathematical algorithms to hardware implementations, particularly benefiting domains like digital signal processing, control systems, and communications where MATLAB is extensively used for algorithm development and verification.

Library Ecosystems and Specialized Frameworks

The significance of HLS is amplified by specialized libraries and frameworks that provide reusable components and domain-specific optimizations. These ecosystems reduce development time while promoting best practices and optimized implementations. The hls4ml framework specifically targets machine learning applications, translating neural networks from popular ML frameworks like TensorFlow and PyTorch into HLS C++. It supports multiple backend synthesis tools including Xilinx Vitis HLS, Intel HLS Compiler, and Cadence Stratus HLS (formerly Catapult HLS), enabling portability across different vendor platforms while maintaining optimization for neural network operations. Complementing domain-specific frameworks, general-purpose libraries like hlslib provide foundational abstractions for HLS development. As a header-only C++ library, hlslib offers templates and utilities for common hardware patterns including FIFOs, reduction trees, shift registers, data packs, and stream interfaces. These abstractions allow developers to work at a higher level of abstraction while ensuring efficient hardware implementation. The AnyHLS project further extends this ecosystem by providing vendor-agnostic interfaces and utilities, though its implementation details vary across different toolchains.

Transformation of Design Methodology

HLS fundamentally changes hardware design methodology by introducing software-like development workflows to hardware creation. The essential characteristics distinguishing HLS code from general-purpose software reflect this transformation: all loops must have compile-time constant bounds, dynamic memory allocation is replaced by static arrays sized for hardware implementation, and parallelism must be explicitly specified through pragmas and coding patterns [13]. These constraints represent the boundary between software flexibility and hardware efficiency, requiring developers to think architecturally about resource utilization and timing while maintaining algorithmic focus. The synthesis process builds concurrent RTL modules from C++ classes or functions, with each synthesized element becoming a clocked process in the resulting RTL [13]. Interface synthesis adds communication protocols to untimed C++ designs, inferring port sizes and directions from the source code while allowing protocol specification through the HLS tool [13]. This automated protocol generation is particularly significant for creating standardized interfaces that integrate with existing IP blocks and system architectures. The methodology enables what is effectively "software-defined hardware," where algorithmic changes can be rapidly evaluated in hardware context, and performance trade-offs can be explored through directive changes rather than architectural redesign.

Enabling New Application Domains

Beyond traditional digital design domains, HLS enables hardware acceleration in fields previously dominated by software implementations. Machine learning inference represents a prime example, where frameworks like hls4ml allow neural networks to be deployed on FPGAs with latency and power characteristics superior to general-purpose processors. Networking applications benefit from P4 integration, enabling programmable packet processing pipelines that can be modified and optimized without complete hardware redesign. Scientific computing and financial analytics leverage HLS for creating custom accelerators tailored to specific computational patterns, achieving performance improvements of orders of magnitude compared to software implementations. The significance of HLS continues to grow as computational demands increase across all technology sectors. By providing a pathway from algorithmic specification to efficient hardware implementation, HLS reduces barriers to hardware acceleration, enables more extensive exploration of design alternatives, and fosters collaboration between software and hardware engineers. As toolchains mature and ecosystems expand, HLS is positioned to become increasingly central to the development of specialized computing systems across diverse application domains.

Applications and Uses

High-Level Synthesis (HLS) has evolved from a niche productivity tool into a critical methodology for addressing the stringent design requirements of modern computing domains. The methodology is particularly vital for creating novel architectures in demanding fields such as Wireless, 5G, AI/ML, Automotive, and Video/Image processing, where design and verification challenges are significant [14]. By abstracting hardware design to higher-level languages, HLS allows engineers to rapidly iterate on Power, Performance, and Area (PPA) trade-offs. Advanced HLS tools incorporate PPA analysis and optimization throughout the synthesis flow, aiming to optimize all three parameters simultaneously while allowing designers to make necessary tradeoffs between power consumption, performance (speed), and chip area [16].

Input Languages and Design Entry

While the C-based synthesis era established subsets of C, C++, and SystemC as foundational input languages, the landscape of HLS inputs has diversified significantly [8]. Today, standard C and C++ remain the most common inputs, but the ecosystem now incorporates code written in Python and more specialized domain-specific languages (DSLs). A prominent example is P4 (Programming Protocol-Independent Packet Processors), a domain-specific language explicitly designed for specifying packet processing algorithms in networking hardware, such as switches and network interface cards [1]. This expansion into DSLs allows for more intuitive and efficient design capture for specialized applications. Furthermore, the "unfamiliar language" issue that once plagued hardware description has been largely mitigated by the adoption of IEEE 1666 SystemC and C++ as the languages of choice for high-level design and verification, making the technology more accessible to software engineers and algorithm developers [8]. A notable workflow for algorithm developers involves MATLAB®. Functions developed in MATLAB can be synthesized into AMD Vitis™ HLS-friendly C++ code via the code generation capabilities of MathWorks HDL Coder. This generated, synthesizable C++ code serves as a direct input to Vitis™ HLS, significantly expediting the translation process from algorithmic simulation to implementable hardware [1]. This bridge between algorithmic modeling environments and HLS tools is crucial for applications in digital signal processing (DSP) and wireless communications, where complex algorithms for 5G beamforming, image processing filters, and video codecs are first modeled and verified in high-level environments.

Optimization Techniques and Overcoming Constraints

A core application of HLS is navigating and overcoming the inherent resource and timing constraints of hardware targets. As noted in practical guides, overcoming resource constraints is a common hurdle, requiring careful resource management to balance high performance with the limitations of available on-chip resources [1]. HLS tools and designers employ several key optimization directives to achieve this:

Resource sharing: Enabling the reuse of functional units (like adders or multipliers) across multiple operations within the HLS code to reduce area footprint [1].
Loop pipelining: Allowing iterations of a loop to overlap, thereby increasing throughput and performance without necessarily requiring more hardware, though it can impact initiation interval and latency [1].
Array partitioning: Breaking large arrays into smaller, distributed blocks (e.g., complete, block, or cyclic partitioning) to reduce memory bandwidth bottlenecks and improve parallel data access times [1].
Bit-width optimization: Reducing the bit-width of variables and operations to the minimum required precision, which saves significant area, reduces power consumption, and can improve timing [1]. Despite these optimizations, achieving timing closure—where all signals in the design meet their required timing specifications—remains a persistent challenge [1]. HLS tools might not always produce Register Transfer Level (RTL) code that meets stringent timing constraints, especially for high-frequency targets, necessitating careful coding styles, constraints, and sometimes manual RTL intervention post-synthesis [1].

Library Ecosystems and Frameworks

The practical adoption of HLS is heavily supported by a growing ecosystem of open-source and commercial libraries and frameworks that provide abstractions, templates, and pre-verified components. These ecosystems reduce development time and promote best practices. Building on the hls4ml framework mentioned previously, which targets machine learning applications, other libraries play crucial roles:

hlslib: This is a header-only C++ library that provides essential abstractions for hardware design common in HLS. It includes constructs for:
- First-In-First-Out (FIFO) buffers with robust stream interfaces. - Reduction trees for efficient parallel accumulation operations. - Shift registers and data packs (SIMD-style operations). - These abstractions help write more portable, readable, and efficient HLS code [1].
AnyHLS: This project, which uses hlslib, aims to create a more vendor-agnostic HLS coding style. It provides wrappers and methodologies to make code more portable across different HLS tool backends, such as those from Xilinx (Vitis HLS), Intel (HLS Compiler), and Cadence (Stratus HLS), mitigating vendor lock-in [1]. These libraries abstract away many low-level implementation details, allowing designers to focus on architecture and algorithm. For instance, using hlslib's stream interfaces can simplify the design of dataflow-style architectures, which are common in video processing pipelines, by managing handshaking and data validity protocols automatically [1].

Application-Specific Case Studies and PPA Exploration

The true power of HLS is realized in its application to specific, complex domains. As noted earlier, the design challenges for Wireless, 5G, AI/ML, Automotive, and Video/Image processing are key drivers for HLS adoption [14]. In these fields, HLS enables rapid exploration of micro-architectures to find an optimal PPA balance for a given algorithm [16]. For example, an engineer designing a convolutional neural network (CNN) accelerator can use HLS to quickly prototype and evaluate different design points: a deeply pipelined, high-throughput architecture versus a more resource-shared, area-efficient one. They can experiment with different array partitioning schemes for feature maps and weights, and apply bit-width optimization to activation and weight data paths to quantify area and power savings [1][16]. This exploratory capability is invaluable for System-on-Chip (SoC) and Multi-Chip Module (MCM) design, where integrating diverse intellectual property (IP) blocks—some potentially generated via HLS—requires careful consideration of interconnect, synchronization, and overall system PPA [15]. HLS facilitates the creation of custom accelerators that are tightly optimized for a specific function within a larger system, a common requirement in automotive systems for sensor fusion or in data centers for specialized compute offload. In conclusion, the applications and uses of HLS extend far beyond simple translation from C to gates. It is a comprehensive methodology for hardware design that encompasses diverse input languages, employs sophisticated optimization techniques to meet constraints, leverages rich library ecosystems for productivity, and enables critical architectural exploration for advanced applications. Its role is central in managing the growing complexity and PPA trade-offs inherent in modern electronic systems [14][16].