Enhanced Neural Network Simulation Using FPGA Coprocessor

1. Chapter 11 in Systems and Circuit Design for Biologically-Inspired Intelligent Learning FPGA Coprocessor for Simulation of Neural Networks Using Compressed Matrix Storage Richard Dorrance Literature Review: 1/11/13

2. Overview • Binary neuron model for unsupervised learning • arranged into minicloumns and macrocloumns • Sparse matrix vector multiplication (SpMxV) • compressed row format • software optimizations • FPGA coprocessor • exploiting matrix characteristics for a “novel” architecture • benchmarks and scalability (or lack there of)

3. Neural Network Example

4. Binary Neuron Model • Modeled as McCulloch-Pitts neurons (i.e. binary): • only 2 states: firing (1) and not firing (0) • fixed time step t • refractory period of one time step

5. Macrocolumn Dynamics

6. Cortical Macrocolumn Connection Matrix

7. Feature Extraction: Bars Test

8. Compress Row Format • Sparse matrix representation (w/ 3 vectors): • value • column index • row pointer

9. SpMxV is the Bottleneck • Theoretically SpMxV should be memory bound • Reality: lots of stalling for data due to irregular memory access patterns • Coding Strategies: • cache prefetching • matrix reordering • register blocking (i.e. N smaller, dense matrices) • CPU: 100 GFLOPS (theoretical), 300 MFLOPS (reality) • GPU: 1 TFLOPS (theoretical), 10 GFLOPS (reality)

10. Simplifications and Optimizations • Matrix elements are binary: value vector is dropped • Strong block-like structure to matrices: compress column index vector

11. FPGA Coprocessor Architecture

12. Resource Usage

13. Scalability

14. Conclusions • SpMxV is the major bottleneck to simulating neural networks • Architectures of CPUs and GPUs limit performance • FPGAs can increase performance efficiency • Specialized formulation of SpMxV  limited scalability