Efficient Flexible Edge Inference for Mixed-Precision Quantized DNN using Customized RISC-V Core

Abstract

This paper presents a customized RISC-V core architecture specifically designed for highly efficient and flexible edge inference of mixed-precision quantized Deep Neural Networks (DNNs). The core utilizes specialized instruction set extensions and architectural modifications optimized to handle varied low-bit quantization schemes (e.g., 2-bit, 4-bit) dynamically across network layers. This innovation significantly improves energy efficiency and computational throughput compared to standard general-purpose cores, enabling practical deployment of aggressive quantization techniques at the extreme edge.

Report

Key Highlights

• Domain Focus: Optimizing inference performance and energy efficiency for Deep Neural Networks deployed at the network edge (resource-constrained environments).
• Core Innovation: Development of a customized RISC-V core leveraging its inherent extensibility for specialized tasks.
• Flexibility & Efficiency: The architecture explicitly supports mixed-precision quantization, allowing dynamic adjustment of bit-widths (e.g., 2-bit, 4-bit, 8-bit) layer-by-layer to maximize efficiency without sacrificing model accuracy.
• Performance Target: Achieving high throughput (operations per second) per watt consumed for highly quantized workloads.

Technical Details

• Architectural Extensions: The customization involves adding specialized instruction set extensions (ISEs) to the standard RISC-V base, particularly focusing on fused multiply-accumulate (MAC) operations.
• Mixed-Precision Datapath: Implementation of a flexible datapath and execution units capable of dynamically configuring their bit-width operation. This often involves packing multiple low-precision operands (e.g., sixteen 2-bit values) into standard 32-bit or 64-bit registers to achieve massive parallelism.
• Quantization Handling: The custom core likely includes specific instructions for efficient packing, unpacking, scaling, and bias application necessary for quantized arithmetic, minimizing the overhead traditionally associated with software-based quantization.
• Memory Interface: Optimization of the load/store unit to efficiently fetch and distribute the densely packed, quantized weight and activation data structures, reducing memory bandwidth pressure.

Implications

• Validation of RISC-V Extensibility: This work serves as a prime example of RISC-V’s foundational strength—the ability to create highly specialized, domain-specific processors (DSPs/Accelerators) without starting from scratch, strongly validating the RISC-V ISA for AI hardware customization.
• Advancing Edge AI Deployment: By efficiently supporting mixed-precision models, the solution removes a major hardware bottleneck. It allows machine learning researchers to utilize the most aggressive quantization strategies possible for latency and power reduction, accelerating the commercial adoption of complex DNNs on battery-powered edge devices.
• Competitive Advantage: The customized core provides a high-performance alternative to proprietary or fixed-function accelerators, positioning the RISC-V ecosystem as a leading platform for energy-efficient, adaptable AI hardware development.