High-Level Synthesis: A Practical Guide to Best Practices

Understanding High-Level Synthesis (HLS)

High-Level Synthesis (HLS) is revolutionizing hardware design, allowing engineers to describe desired behavior in high-level languages like C, C++, or SystemC, rather than low-level Register Transfer Level (RTL) code. This shift significantly boosts productivity and opens doors to faster innovation.

• HLS is a design methodology and automated process that translates high-level language descriptions of hardware behavior into RTL implementations.

• It abstracts away the complexities of manual RTL coding, enabling designers to focus on algorithmic optimization and system-level architecture.

• HLS bridges the gap between software and hardware development, making it easier for software engineers to participate in hardware design.

• Historically, hardware design relied on RTL languages like Verilog and VHDL, demanding meticulous, cycle-accurate coding.

• This process was time-consuming, error-prone, and required specialized expertise, creating a bottleneck in development cycles.

• HLS emerged as a solution, automating the RTL code generation and verification, and freeing up engineers to explore design space.

• Reduced Development Time: HLS slashes development time by allowing designers to specify functionality at a higher level of abstraction.

• Improved Performance: HLS compilers can automatically explore various microarchitectures and optimizations, often leading to better performance than hand-optimized RTL.

• Design Space Exploration: HLS enables rapid prototyping and exploration of different design options, allowing engineers to identify the optimal solution for their application.

• HLS enables engineers to implement hardware designs that can improve the critical thinking of systems Teachers’ (evolving) beliefs about critical thinking education during professional learning: A multi- case study.

• In healthcare, HLS can accelerate the development of custom hardware for medical imaging and diagnostics.

• In retail, it can enable real-time fraud detection and personalized recommendations on edge devices.

• In finance, HLS can speed up high-frequency trading algorithms and risk management systems.

A 2023 report estimates that HLS can reduce hardware development time by up to 50% compared to traditional RTL design.

With a solid understanding of HLS, we can now delve into the design flow and best practices that make this technology truly powerful.

Selecting the Right HLS Tools

Selecting the right High-Level Synthesis (HLS) tool is like choosing the perfect instrument for a complex symphony—the outcome hinges on the tool's capabilities and how well it fits your unique needs. But with multiple options available, how do you make the right choice?

Selecting the appropriate HLS tool involves carefully assessing several critical factors. These considerations ensure that the chosen tool aligns with your project's specific requirements, optimizing performance and efficiency.

• Performance Targets: Do you need raw speed, minimal latency, or optimized power consumption? Different HLS tools excel in different areas. For example, in high-frequency trading, where latency is paramount, you'd need a tool that prioritizes speed and throughput.
• Target Technology (FPGA vs. ASIC): Field-Programmable Gate Arrays (FPGAs) offer flexibility and faster prototyping, while Application-Specific Integrated Circuits (ASICs) provide optimized performance and power efficiency for high-volume production. Ensure the HLS tool you select can generate code compatible with your chosen deployment technology.
• Language Support (C/C++, SystemC, OpenCL): The high-level language you use to describe your hardware behavior determines which HLS tools are compatible. C and C++ are widely supported, but SystemC offers advanced modeling capabilities, and OpenCL targets heterogeneous computing platforms.
• Verification Capabilities: Thorough verification is essential to ensure your HLS design functions correctly. Look for tools that offer robust simulation, formal verification, and co-simulation capabilities to catch errors early in the design process.
• Integration with Existing Workflows: HLS tools must integrate seamlessly with your existing design environment, including simulation, synthesis, and implementation tools. Compatibility with industry-standard formats like SystemVerilog and VHDL is crucial for a smooth transition.

Several leading HLS tools are available, each with its own strengths and weaknesses. Understanding these tools can help you make an informed decision:

• Xilinx Vivado HLS: This tool is tightly integrated with Xilinx FPGAs, offering excellent performance and optimization for Xilinx devices. It supports C, C++, and SystemC, and provides extensive verification and debugging capabilities.
• Intel HLS Compiler: Designed for Intel FPGAs, this compiler optimizes code for Intel's architecture, focusing on performance and power efficiency. It supports OpenCL, enabling software engineers to easily target Intel FPGAs.
• Cadence Stratus HLS: Stratus HLS targets both FPGAs and ASICs, providing a comprehensive solution for hardware design. It supports C, C++, and SystemC, and offers advanced features for design space exploration and optimization.
• Mentor Catapult HLS: Catapult HLS focuses on ASIC design, offering high-level synthesis and verification capabilities. It supports C, C++, and SystemC, and provides advanced power analysis and optimization features.

Pricing and licensing models vary among these tools, so consider your budget and project requirements when making your selection.

Choosing the right HLS tool is a critical decision that can significantly impact your hardware design project's success. As you move forward, the next section will explore design flow and best practices for using HLS effectively.

Algorithm Optimization for HLS

Algorithm optimization in High-Level Synthesis (HLS) unlocks the full potential of custom hardware, but efficient algorithm design is crucial for achieving top-tier performance. But how can algorithm optimization be effectively applied for HLS?

• Loop Optimization: Loops are the workhorses of many algorithms, and HLS tools provide various techniques to enhance their performance.

  • Loop Unrolling increases parallelism by replicating the loop body, potentially executing multiple iterations concurrently. For example, a loop processing video frames could be unrolled to process several frames simultaneously.
  • Loop Pipelining allows the initiation of a new loop iteration before the previous one completes, improving throughput. Consider a healthcare application where an HLS-implemented loop processes sensor data—pipelining could ensure continuous data flow.

• Dataflow Optimization: This technique focuses on maximizing the flow of data between operations.

  • By strategically scheduling tasks and memory accesses, HLS can create efficient data pipelines. For example, in retail, an HLS-implemented algorithm for fraud detection could use dataflow optimization to process transaction data with minimal delay.

• Memory Access Optimization: Efficient memory access is vital for performance.

  • HLS can optimize access patterns to reduce latency and bandwidth requirements. In finance, an HLS-implemented algorithm for risk management might use memory access optimization to quickly retrieve and process large datasets.

The way you write your code significantly impacts the final hardware implementation. By adopting specific coding styles, you can guide the HLS tool towards generating more efficient RTL.

• Exploiting Parallelism: HLS excels at exploiting parallelism. By expressing your algorithm in a way that exposes independent operations, you allow the HLS tool to generate parallel hardware. Imagine an image processing algorithm where different pixels can be processed concurrently—structuring the code to reflect this parallelism can drastically improve performance.

As you optimize your algorithms, the next step is to carefully manage data types and precision. This balance is key to achieving both accuracy and efficiency in your HLS designs.

Verification and Validation in HLS

Verification and validation are crucial in High-Level Synthesis (HLS) to ensure the generated hardware functions correctly and meets performance goals. Without these steps, subtle bugs can lead to costly redesigns later in the development cycle.

Effective verification addresses several key challenges in HLS designs:

• Functional Correctness: HLS designs must perform the intended operations accurately. Simulation, co-simulation, and formal verification can help catch logical errors early. Imagine a healthcare application: an error in an HLS-generated image processing unit could lead to misdiagnosis.
• Performance Constraints: HLS designs must meet timing, power, and area requirements. Simulation and hardware emulation allow designers to validate these constraints. For instance, in high-frequency trading, failing to meet latency targets would render the design useless.
• Complexity: HLS introduces new levels of abstraction, making manual inspection difficult. Thorough verification is essential to manage this complexity and avoid unexpected behavior. Consider an automotive application: an HLS-generated control system failure could have catastrophic consequences.

As these challenges illustrate, verification is essential to the success of HLS projects.

Simulation and co-simulation are essential for verifying HLS designs at different levels of abstraction:

• C/C++ Simulation: This involves simulating the HLS code in a software environment to check its functionality. This step helps detect algorithmic errors before hardware implementation. For example, in retail, simulating an HLS-implemented recommendation engine can ensure accurate product suggestions.
• Transaction-Level Modeling (TLM): TLM models system-level interactions, allowing for faster simulation and exploration of different architectures. TLM is valuable for telecommunications systems where designers need to validate complex dataflows.
• Co-simulation with RTL Simulators: This technique combines C/C++ simulation with Register Transfer Level (RTL) simulation to verify the generated hardware code. Co-simulation ensures the RTL implementation matches the high-level specification. This is critical in aerospace, where hardware must function precisely.
• Hardware Emulation: Emulation uses specialized hardware to mimic the behavior of the target device, allowing for near real-time verification. Hardware emulation can validate complex cryptographic algorithms with high accuracy.

Formal verification employs mathematical techniques to prove the correctness of HLS designs:

• Functional Correctness Proof: Formal methods can mathematically verify that the generated RTL code implements the intended functionality. This approach is useful in safety-critical systems where errors are unacceptable.
• Assertion-Based Verification: This involves adding assertions to the HLS code that specify expected behavior. Formal tools then check these assertions to ensure they hold true.
• Model Checking: Model checking explores all possible states of the design to verify that it meets certain properties. This method is particularly useful for verifying complex control logic.

As you can see, verification and validation are critical to ensure the success of HLS designs. The next section delves into best practices for managing data types and precision in HLS.

Overcoming Common HLS Challenges

Overcoming resource constraints is a common hurdle in High-Level Synthesis (HLS), but smart strategies can help you design efficient hardware. Let's dive into practical ways to tackle these challenges.

One of the primary challenges is balancing the need for high performance with the limitations of available resources. Often, you'll need to make trade-offs to achieve an optimal design.

• Identify critical sections: Determine which parts of your algorithm have the biggest impact on overall performance. Focus your optimization efforts there.
• Resource sharing: Enable resource sharing within the HLS code. For example, share a single multiplier among multiple operations that do not execute concurrently.

Several techniques help reduce the resource footprint of your HLS designs.

• Loop pipelining: As noted in the earlier section, this allows operations to overlap, increasing throughput without requiring more hardware.
• Array partitioning: Break large arrays into smaller blocks to reduce memory bandwidth and improve access times.
• Bit-width optimization: Reduce the precision of variables to the minimum required for the application. This can significantly reduce the size of arithmetic units.

In HLS, the relationship between area and time is often inversely proportional. You can reduce the area by increasing the execution time, and vice versa.

• Increase latency: Allow an operation to take more clock cycles to complete, reducing the need for parallel hardware.
• Reduce parallelism: Decrease the number of concurrent operations to lower resource usage.

While specific company examples are not available in the search context, consider a video processing application. By carefully managing memory access patterns and using loop optimizations, you can achieve real-time performance without exceeding the resource limits of your target FPGA.

Efficient resource management is essential for successful HLS designs. In the next section, we'll explore techniques for managing timing closure.

HLS in Action: Case Studies

High-Level Synthesis (HLS) presents unique challenges that hardware engineers must understand to fully leverage its power. Navigating these complexities effectively is essential for optimizing designs and achieving desired performance goals.

• Resource Management: HLS designs often face constraints in terms of available resources such as memory, logic gates, and power.
  • Engineers must carefully manage these resources while ensuring the design meets its performance targets.
  • For instance, in edge computing applications, power consumption is a critical constraint, requiring designs that minimize energy use without sacrificing performance.
• Timing Closure: Achieving timing closure, where all signals meet their timing requirements, can be challenging in HLS designs.
  • HLS tools might not always produce Register Transfer Level (RTL) code that meets stringent timing specifications.
  • In high-speed communication systems, precise timing is crucial, and failing to meet these requirements can lead to system malfunctions.
• Verification Complexity: Verifying the correctness of HLS designs can be complex due to the high level of abstraction.
  • Simulation and formal verification techniques are essential to ensure the generated RTL code accurately implements the intended functionality.
  • In safety-critical systems, such as those used in automotive or aerospace applications, thorough verification is paramount to prevent potentially catastrophic failures.

Consider a scenario where you're using HLS to design a custom image processing pipeline for a surveillance system. You might face challenges in balancing the need for high throughput with the limited memory and processing power available on the edge device. To overcome this, you could explore techniques like loop pipelining and dataflow optimization to maximize data throughput while minimizing resource utilization.

// Example of loop pipelining in C++
void image_processing(int *input_buffer, int *output_buffer, int width, int height) {
for (int i = 0; i < height; i++) {
#pragma HLS pipeline
for (int j = 0; j < width; j++) {
output_buffer[i * width + j] = process_pixel(input_buffer[i * width + j]);
}

By addressing these challenges head-on with strategic algorithm optimization, careful management of data types, and rigorous verification, you can unlock the full potential of HLS and create custom hardware that meets your specific application needs.

The next section delves into real-world case studies that showcase HLS in action.

The Future of High-Level Synthesis

Is High-Level Synthesis (HLS) poised to become the standard for hardware design, or will it remain a niche tool? The answer lies in understanding the emerging trends and the democratization of hardware design that HLS enables.

The future of HLS involves several exciting developments. These advancements promise to further streamline the hardware design process and expand the scope of HLS applications.

• AI-driven HLS: Artificial intelligence (AI) is set to play a significant role in optimizing HLS flows. Imagine AI algorithms automatically tuning HLS compiler settings, exploring design spaces, and even suggesting algorithmic improvements.
• Cloud-based HLS: Cloud platforms offer scalable compute resources and collaborative environments that are well-suited to tackle complex HLS designs. Designers can access powerful HLS tools remotely, share designs, and accelerate the development process.
• Integration with new hardware architectures: HLS is already adapting to emerging hardware architectures. Think of HLS compilers generating code optimized for chiplets, which are modular integrated circuits, enabling designers to create custom systems with unprecedented flexibility.

One of the most profound impacts of HLS is its potential to democratize hardware design. By raising the level of abstraction, HLS makes hardware development more accessible to a wider range of engineers.

• Making hardware design accessible to software engineers: HLS allows software engineers, already proficient in high-level languages, to contribute to hardware design. This collaboration bridges the gap between software and hardware teams, fostering innovation and accelerating development cycles.
• Reducing the barrier to entry for custom hardware acceleration: Historically, custom hardware design required specialized expertise in RTL languages. HLS lowers this barrier, enabling smaller companies and research teams to design custom hardware for specific applications.
• Enabling faster innovation in various industries: As hardware design becomes more accessible, innovation is set to accelerate across diverse industries. In telecommunications, HLS can enable the rapid development of custom hardware for 5G and beyond.

As Teachers’ (evolving) beliefs about critical thinking education during professional learning: A multi- case study indicates, educators are already seeing the importance of critical thinking in system design.

These trends suggest a future where hardware design is no longer the domain of a select few.

As HLS continues to evolve, the industry will need to address challenges such as verification complexity and power optimization. The final section will explore best practices for managing these challenges and ensuring successful HLS deployments.

LogicClutch: Your Partner in Enterprise Technology Consulting

High-Level Synthesis (HLS) offers a promising path to efficient hardware design, but navigating its complexities requires expert guidance. LogicClutch stands ready to assist enterprises in harnessing the full potential of HLS technology.

LogicClutch provides tailored solutions for complex enterprise challenges. We specialize in Master Data Management, Salesforce CRM Solutions, and AI analytics.

• Our team of experienced consultants possesses deep industry knowledge and technical expertise. We understand the unique challenges businesses face when integrating new technologies like HLS.
• LogicClutch has a proven track record of successful project delivery. We ensure that your technology investments align with your strategic goals and deliver measurable results.

We offer end-to-end technology consulting, from initial consultation to implementation. LogicClutch meets your specific business requirements.

• Our services include on-demand development and resource augmentation, providing the support you need to accelerate your HLS projects.
• We also offer AI-powered SaaS solutions, custom development, data management, edge computing, and computer vision AI capabilities.
• LogicClutch helps you build robust and scalable solutions optimized for your target technology.

LogicClutch is committed to innovation and client satisfaction. Our approach is designed to ensure that adopting HLS leads to tangible business benefits.

• We bring a deep understanding of HLS design flow and best practices. This enables us to guide you through the entire process, from algorithm optimization to verification and validation.
• LogicClutch helps you overcome common HLS challenges like resource constraints and timing closure. This ensures that your designs are efficient, reliable, and meet your performance targets.
• We build hardware designs that can improve the critical thinking of systems Teachers’ (evolving) beliefs about critical thinking education during professional learning: A multi- case study.

As you consider the future of your hardware design, remember that LogicClutch is here to help you navigate the path to success with HLS. Contact us today to learn more about how our expertise can elevate your enterprise.