Approximate Computing in High-Level Synthesis: From Survey to Practical Implementation

Abstract

This paper analyzes the integration of Approximate Computing (AC) techniques within the High-Level Synthesis (HLS) design flow. It first provides a comprehensive survey of existing approximation methodologies applicable at the behavioral level, detailing their potential benefits in area, power, and speed. Crucially, the work presents a practical implementation framework, demonstrating how these techniques can be systematically applied during HLS to automatically generate highly efficient, accuracy-controllable hardware accelerators.

Report

Key Highlights

• Dual Focus: The research combines a comprehensive survey of Approximate Computing (AC) strategies suitable for hardware optimization with the development of a practical, automated implementation framework within the HLS flow.
• Automation of Trade-offs: The core innovation lies in automating the insertion and optimization of AC techniques directly into the HLS process, enabling designers to quickly explore the trade-off space between computational accuracy and hardware efficiency.
• Efficiency Gains: The methodology targets significant improvements in hardware metrics, including reduced power consumption, smaller circuit area, and higher operational speed (latency reduction).
• Behavioral-Level Approximations: The focus is on implementing approximations at high levels of abstraction (C/C++), which maximizes the potential design space exploration before RTL generation.

Technical Details

• Integration Flow: The proposed implementation likely involves new compilation or optimization passes integrated into the HLS toolchain. This typically includes a sensitivity analysis pass to identify sections of code where approximation error has minimal impact on the overall output quality.
• Specific Techniques: The practical implementation likely utilizes and evaluates one or more of the following HLS-compatible techniques:
  • Operator Substitution: Replacing exact arithmetic units (e.g., high-precision multipliers) with structurally approximate counterparts to save area and power.
  • Data Approximation: Techniques such as bit-width reduction, fixed-point truncation, or dynamic precision scaling applied to variables identified as non-critical.
  • Control Flow Modification: Automated restructuring of loops (e.g., loop perforation or approximate iteration skipping) or conditional blocks based on error tolerance.
• Evaluation Metrics: The practical demonstration requires establishing quantitative relationships between approximation metrics (e.g., output quality degradation like PSNR or MSE) and hardware improvements (e.g., reduction in estimated ASIC area or FPGA resource usage).

Implications

• RISC-V Accelerator Enablement: This methodology directly supports the rapid development of specialized, energy-efficient accelerators for the RISC-V ecosystem. By lowering the effort required to create custom hardware from high-level languages, it broadens the viability of RISC-V for domain-specific architecture (DSA) deployment in AI, signal processing, and IoT.
• Competitive Edge in Edge Computing: Integrating AC early in the HLS design phase allows designers to meet stringent power and area budgets required for competitive edge and mobile RISC-V implementations, offering superior energy-per-operation compared to general-purpose solutions.
• Faster Design Cycles: By automating complex approximation tasks that traditionally required manual RTL modification, the approach significantly shortens the design and verification cycle for custom RISC-V hardware, accelerating time-to-market.
• Foundation for Future ISA Extensions: The highly optimized approximate blocks generated via this AC-HLS approach could serve as ideal candidates for defining new RISC-V Custom Instruction Set Architecture (ISA) extensions, thereby boosting performance for specific application domains.