C.O.R.A

FPGA-Accelerated Neuromorphic Keyword Spotting: Comprehensive Project Plan

Table of Contents

• Project Overview
• Technical Architecture
• Project Plan with Timeline
• Key Technical Challenges and Solutions
• Performance Metrics and Evaluation
• Tools and Resources

Project Overview

This comprehensive project plan outlines a structured approach to developing an FPGA-accelerated neuromorphic keyword spotting system. By following this phased approach, the project will systematically address the challenges of bio-inspired audio processing, spiking neural networks, and efficient FPGA implementation. The resulting system promises to deliver real-time performance with the power efficiency benefits of neuromorphic computing, suitable for edge AI applications in voice interfaces and always-on devices.

The project leverages cutting-edge techniques from recent research in neuromorphic engineering , event-based processing , and bio-inspired auditory models , while providing concrete milestones and deliverables to ensure successful completion.

Key innovations include:

· A 64-channel bio-inspired cochlear frontend with precise spike timing (1μs precision) · An FPGA-optimized SNN architecture with STDP learning capabilities · End-to-end neuromorphic processing pipeline from audio to keyword recognition · Real-time performance with minimal power consumption

Technical Architecture

1. Cochlear Audio Frontend (Biological Signal Processing Layer)

64-band FIR filter bank (200Hz-8kHz):

· Implement a logarithmic frequency scale mimicking human cochlea · Use optimized FIR structures for FPGA implementation · Coordinate Rotation Digital Computer (CORDIC) algorithm for efficient trigonometric calculations

Leaky Integrate-and-Fire (LIF) spike encoding:

· 1μs timing precision for temporal coding · Address-Event Representation (AER) output format (16-bit spike packets) · Hardware-friendly implementation using base-2 terms and simplified multiplication operations

2. SNN Processing Core (Neuromorphic Computation Layer)

Input Layer:

· 64 neurons corresponding to cochlear frequency channels · Direct mapping from AER packets to neural inputs

Hidden Layer:

· 128 LIF neurons with configurable time constants · Spike-Timing Dependent Plasticity (STDP) learning · Depthwise separable architecture for efficient implementation

Output Layer:

· One neuron per keyword class · Decision based on first-spike timing or population coding

3. FPGA Implementation

Hardware Acceleration:

· Parallel processing of filter banks · Pipelined spike processing · Memory-optimized weight storage · Event-graph neural network approach for efficient processing

Interface:

· PDM microphone input compatibility · AER output for system integration · Configuration interface for parameter tuning

Project Plan with Timeline

Phase 1: Research & Specification (Weeks 1-4)

Tasks:

· Literature review of cochlear models and SNN architectures · Study existing FPGA implementations of neuromorphic systems · Define detailed system specifications · Select optimal FPGA platform (ZedBoard) · Setup development environment (Python, PyTorch, Vivado)

Deliverables:

· Detailed technical specification document · Toolchain setup and verification · Initial prototype of cochlear model in Python

Phase 2: Cochlear Model Development (Weeks 5-8)

Tasks:

· Implement 64-band filter bank in Python · Develop LIF spike encoding algorithm · Optimize for hardware implementation · Create AER packet format specification · Begin Verilog implementation of key components

Deliverables:

· Functional Python model of cochlear frontend · Preliminary Verilog modules for FIR filters · Testbench for spike encoding verification

Phase 3: SNN Development (Weeks 9-12)

Tasks:

· Design SNN architecture in PyTorch · Implement STDP learning rule · Train network on target keywords · Quantize model for FPGA implementation · Develop spike processing algorithms

Deliverables:

· Trained SNN model with acceptable accuracy · Quantized version suitable for FPGA · Verification test cases

Phase 4: RTL Implementation (Weeks 13-16)

Tasks:

· Complete Verilog implementation of cochlear frontend · Implement SNN processing core · Design memory subsystem for weights · Create system control logic · Develop interfaces (audio in, AER out)

Deliverables:

· Complete RTL codebase · Synthesis reports · Functional simulation results

Phase 5: FPGA Synthesis & Optimization (Weeks 17-20)

Tasks:

· Synthesize design for target FPGA · Perform timing closure · Optimize for power and area · Validate real-time performance · Characterize resource utilization

Deliverables:

· Bitstream for target FPGA · Performance characterization report · Power consumption estimates

Phase 6: Verification & Testing (Weeks 21-24)

Tasks:

· Develop comprehensive testbench · Verify against Python reference model · Test with real audio inputs · Measure keyword spotting accuracy · Characterize latency and throughput

Deliverables:

· Verification report · Performance metrics · Demonstration setup

Phase 7: Demo & Final Report (Weeks 25-26)

Tasks:

· Prepare demonstration system · Create project documentation · Finalize all reports · Prepare presentation materials

Deliverables:

· Working demonstration · Complete project documentation · Final presentation

Key Technical Challenges and Solutions

1. Real-time Processing Constraint: · Use parallel filter bank implementation · Optimize spike processing pipeline · Leverage FPGA's parallel processing capabilities
2. Power Optimization: · Implement depthwise separable architectures · Use event-based processing to minimize activity · Clock gating and power-aware design techniques
3. Learning Implementation: · Approximate STDP for hardware efficiency · Fixed-point arithmetic optimization · On-chip learning vs. pre-trained weights tradeoff
4. System Integration: · Standardized AER interface · Careful clock domain crossing design · Robust synchronization mechanisms

Performance Metrics and Evaluation

1. Accuracy: · Keyword detection accuracy (>90% target) · False positive/negative rates
2. Timing: · End-to-end latency (<100ms target) · Real-time throughput (1x real-time)
3. Resource Utilization: · FPGA resource usage (LUTs, FFs, BRAMs) · Maximum achievable clock frequency
4. Power Efficiency: · Power consumption per inference · Comparison to conventional DSP implementations

Tools and Resources

Software:

· Python/PyTorch for algorithm development and training · Vivado for FPGA synthesis and implementation · HuggingFace for potential pre-trained models

Hardware:

· ZedBoard (Xilinx Zynq-7000) for implementation · PDM microphone for audio input · Measurement equipment for characterization

Reference Designs:

· FPGA implementations of DS-BTNN accelerators · Neuromorphic audio processing pipelines · Cochlea digital circuit models