Computer Vision Model Quantization for RISC-V Edge Processors

Overview of Technical Issues:

The RISC-V computing unit provides insufficient computational acceleration for quantized computer vision operations due to immature optimization libraries and instruction-level support gaps compared to mainstream architectures, resulting in inference speeds that fail to meet edge deployment real-time requirements; the goal is to achieve efficient model quantization that maintains acceptable accuracy while enabling practical inference performance on RISC-V edge processors under strict power and memory constraints.

Solution directions generated for this problem

Problem Direction 1 :

ImproveInstruction execution throughput for quantized ops
VS
ConstraintPower consumption

Inspiration 1 : Cross-domain reference

Application Principle: #35 Parameter changes
Cross-domain applicability Assess applicability
Enhancing processing performance of a DNN module by bandwidth control of fabric interface
Innovative Solution Refine solution

Workload-adaptive voltage-frequency co-optimization for RISC-V quantized inference

Adapt operating voltage and frequency to workload intensity in real-time
How to solve :
  • Implement dynamic voltage-frequency scaling (DVFS) with three operating modes: eco-mode (0.6V/400MHz, <1.5W) for simple frames, balanced-mode (0.8V/800MHz, 2.5W) for typical workloads, and turbo-mode (1.0V/1.2GHz, 4.8W) for complex scenes requiring <50ms latency
  • Deploy workload complexity predictor using lightweight CNN analyzing first 10% of frame data to classify into low/medium/high complexity bins within 2ms, triggering mode transition before main inference
  • Integrate on-chip power monitor sampling current at 10kHz with ±50mA tolerance, enforcing 2-5W average power budget via closed-loop feedback that caps turbo-mode duty cycle to 30% per second and forces eco-mode if rolling 100ms average exceeds 5.2W
Expected Effect : Throughput +180% in turbo vs eco; average power 2.8W; 95% frames <50ms
Risk Control :
  • voltage transition glitches causing computation errors
  • predictor misclassification leading to mode mismatch
  • thermal lag between power spike and throttling response

Problem Direction 2 :

ImproveOptimization library maturity
VS
ConstraintMemory footprint

Inspiration 1 : Cross-domain reference

Application Principle: #10 Preliminary action
Cross-domain applicability Assess applicability
Movement route searching apparatus and movement route searching method
Innovative Solution Refine solution

Offline operator pre-fusion with compact metadata encoding for zero-runtime memory overhead

Pre-fuse operators during model compilation phase to eliminate runtime buffer allocation
How to solve :
  • Perform offline operator fusion analysis during model compilation — identify all fusible conv→ReLU→pooling sequences and generate pre-fused kernel code with zero intermediate buffers
  • encode fusion metadata as compact 8-byte descriptors (4-bit opcode, 12-bit address offset, 16-bit dimension) stored in 16KB lookup table, achieving 80%+ coverage within 128KB total
  • implement in-register dataflow execution where fused operations pass results through 32×INT8 SIMD registers (256 bytes) instead of memory buffers, eliminating 90% of intermediate storage
Expected Effect : Fusion coverage 40%→85%, memory overhead <128KB, redundant loads reduced 70%
Risk Control :
  • fusion pattern coverage incomplete for dynamic models
  • metadata encoding errors causing kernel mismatch
  • register pressure causing spill to memory

Problem Direction 3 :

ImproveInference computation speed
VS
ConstraintPower consumption

Inspiration 1 : Cross-domain reference

Application Principle: #35 Parameter changes
Cross-domain applicability Assess applicability
Cooperative techniques for radio resource control state management in dual-connectivity architectures
Innovative Solution Refine solution

Adaptive voltage-frequency scaling with workload-driven power gating for RISC-V inference acceleration

Workload-driven voltage-frequency scaling optimizes power per frame complexity
How to solve :
  • Implement real-time workload profiler that classifies incoming CV frames into three complexity tiers (low/medium/high) within first 5ms using lightweight feature extraction
  • Configure dynamic voltage-frequency operating points: low-complexity frames run at 0.8V/800MHz (1.2W average), medium at 1.0V/1.2GHz (2.8W), high at 1.2V/1.6GHz (4.5W peak), with transition latency <2ms via on-chip voltage regulator
  • Apply predictive power gating to unused SIMD lanes and cache banks based on profiled workload—shut down 50% functional units for low-complexity frames, achieving 40-60% dynamic power reduction while maintaining <50ms latency through selective resource activation
Expected Effect : Inference speed <45ms, average power 2.3W, 55% energy savings vs full-speed operation
Risk Control :
  • workload classification accuracy <85% causes suboptimal voltage selection
  • voltage transition glitches during rapid frequency changes
  • thermal hotspots from burst high-frequency operation

Problem Direction 4 :

ImproveModel accuracy retention after quantization
VS
ConstraintMust not deteriorate

Inspiration 1 : Cross-domain reference

Application Principle: #10 Preliminary action
Cross-domain applicability Assess applicability
Image processing apparatus and method
Innovative Solution Refine solution

Offline quantization-aware training with pre-compensated weight calibration for RISC-V edge inference

Train model with quantization simulation then deploy optimized weights
How to solve :
  • Perform quantization-aware training offline: inject simulated INT8 noise into forward/backward passes, allowing model weights to adapt to low-precision representation during 50-100 training epochs
  • Execute post-training calibration using 500-1000 representative samples: solve constrained optimization to find INT8 weight values minimizing KL-divergence between FP32 and INT8 activation distributions, achieving <1.5% accuracy drop
  • Package pre-compensated INT8 weights in RISC-V-optimized layout (NHWC format, 64-byte cache-line aligned) and store in on-chip SRAM, eliminating runtime quantization overhead and enabling direct INT8 MAC operations
Expected Effect : Inference speed 150ms→45ms; accuracy drop 3-5%→1.2%; power <2.8W
Risk Control :
  • calibration dataset representativeness insufficient
  • weight quantization clipping threshold suboptimal
  • activation distribution shift in deployment
Patsnap Eureka Solution