ENTERPRISE AI ANALYSIS
CoDAC: Algorithm-Hardware Co-Design by AutoML with Compression for Efficient Edge AI
This paper introduces CoDAC, an AutoML-based framework for joint algorithm-hardware co-design, targeting lightweight NN compression and FPGA acceleration. It delivers significant reductions in resource usage and latency with minimal accuracy loss, achieving Pareto-optimal performance for diverse scenarios.
Executive Impact Summary
CoDAC's approach to AI at the edge significantly boosts efficiency and accelerates deployment. By unifying algorithmic compression and hardware acceleration, it achieves superior system performance, reducing costs and accelerating time-to-market for AI-powered edge devices.
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Resource Utilization Reduced
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Latency Reduced
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Accuracy Degradation
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
CoDAC combines model pruning, quantization, and hardware-oriented optimization within a closed-loop AutoML pipeline. It uses fast, surrogate hardware feedback and multi-objective Bayesian optimization to achieve optimal balances among accuracy, latency, and hardware resources for FPGA accelerators.
CoDAC's core innovations include hardware-aware AutoML with compression, advanced multi-objective co-optimization using Bayesian optimization and surrogate models, and an automated accelerator implementation toolchain. This ensures models are not only compact and accurate but also tailored for optimal hardware performance.
CoDAC: Automated Algorithm-Hardware Co-Design Flow
Input & Edge AI Demands
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Hardware-Aware Multi-Objective Compression (AutoML Search)
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Automated Model-to-Hardware Implementation
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Co-Optimised Accelerator Output
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R-squared for DSP usage prediction by MoE, indicating high accuracy in hardware metric prediction.
| Feature | CoDAC | Traditional |
|---|---|---|
| Optimization Scope | Joint Algorithm-Hardware | Algorithm/Hardware Isolated |
| Feedback Mechanism | Fast Surrogate Hardware Feedback | Slow Full Synthesis |
| Optimization Goal | Pareto-Optimal Balance (Acc/Latency/Resources) | Software-Centric Metrics (FLOPs, Params) |
| Deployment | Automated Toolchain for FPGA | Manual & Specialized Expertise |
Case Study: Resource Savings on Zynq-7020
On the Xilinx Zynq-7020 FPGA, CoDAC demonstrated a 70% reduction in resource utilization and a 12% reduction in inference latency, with only 1.01% accuracy degradation. This was achieved through 11-bit quantization and a reuse factor of 27, dropping DSP usage from 655 to 51 units while maintaining high accuracy for an ultra-lightweight ResNet model on the SVHN dataset.
Project Your Enterprise AI ROI
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Your CoDAC Implementation Roadmap
A phased approach to integrate co-design AutoML into your enterprise, ensuring efficient and impactful AI deployment.
Phase 1: Model Compression & Initial Tuning
Applying pruning and quantization strategies based on preliminary hardware feedback.
Phase 2: Multi-Objective Co-Optimization
Leveraging AutoML with Bayesian optimization and MoE models to explore the joint algorithm-hardware space.
Phase 3: Automated Hardware Synthesis & Deployment
Converting optimized models into synthesizable FPGA accelerators with hardware-specific optimizations (e.g., FIFO depth tuning).
Phase 4: Validation & System Integration
Empirical validation of deployed models on target FPGAs, ensuring functional correctness and performance targets.
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