Abstract

This study investigates dynamic hardware approximation techniques integrated into a specialized RISC-V core to improve energy efficiency in energy-constrained embedded systems. The platform achieved an average energy efficiency of 13.3 pJ/instruction at 500MHz in 45nm CMOS technology. Overall, the approach resulted in a 9.21% improvement in energy efficiency and a 9.23% reduction in overall power consumption compared to accurate circuits.

Report

Structured Report: Evaluation of Run-Time Energy Efficiency using Controlled Approximation in a RISC-V Core

Key Highlights

• Significant Energy Gain: The implementation of dynamic hardware approximation resulted in a 9.21% improvement in overall energy efficiency compared to standard, accurate circuits.
• Power Reduction: The approximate computing techniques led to a 9.23% decrease in overall power consumption.
• Multiplication Efficiency: Energy efficiency for multiplication instructions (a common power bottleneck) saw a dramatic 60.83% improvement.
• Performance Metric: The resulting platform achieved an impressive average energy efficiency figure of 13.3 pJ/instruction.
• Execution Stage Optimization: The execution stage specifically benefited from a 14.64% gain in efficiency due to approximation.

Technical Details

• Architecture: The study utilizes a specialized RISC-V embedded processor that incorporates features specifically designed to enable and control approximate computing.
• Methodology: The approximation is implemented using dynamic hardware methods, allowing the system to trade accuracy for energy savings at runtime.
• Technology Node: The core was synthesized and evaluated in 45nm CMOS technology.
• Operating Frequency: The measured efficiency results were achieved while running the core at a 500MHz clock frequency.
• Target of Approximation: The techniques primarily target core arithmetic operations, yielding the highest benefits in multiplication instructions.

Implications

This research is significant for advancing the utility of RISC-V in energy-critical applications like IoT, wearable devices, and edge computing.

• Enhanced Embedded Competitiveness: By integrating controlled approximation features directly into the hardware layer, RISC-V cores become highly optimized solutions for embedded systems where battery life and thermal limits are paramount, allowing them to deliver higher performance within stringent power envelopes.
• Validation of Approximate Computing: The substantial energy gains, particularly in complex operations like multiplication, validate approximate computing as a viable, practical technique for real-world silicon implementations, moving it beyond purely theoretical or architectural proposals.
• New Design Paradigm: The findings suggest that future RISC-V profiles targeting error-tolerant workloads (such as machine learning inference or signal processing) may adopt dedicated approximate hardware as a standard feature, offering developers explicit control over the accuracy-power trade-off.