Annual: 2019

AP004 »
Neuroevolved Binary networks accelerated by FPGA
📁Machine Learning
👤Raul Valencia
 (University of Auckland)
📅Oct 25, 2019
Regional Final

👀 2945   💬 1

AP004 » Neuroevolved Binary networks accelerated by FPGA


With the explosive interest in the utilization of Neural Networks (NN), several approaches have taken place to make them faster, more accurate or power efficient; one technique used to simplify inference models is the utilization of binary representations for weights, activations, inputs and/or outputs. This competition entry will present a novel approach to train from scratch Binary Neural Networks (BNN) using neuroevolution as its base technique (gradient descent free) executed on Intel FPGA platforms to achieve better results than general purpose GPUs

Traditional NN uses different variants of gradient descent to train fixed topologies, as an extension to that optimization technique, BNN research has focused on the application of such algorithms to discrete environments, with weights and/or activations represented by binary values (-1,1). It has been identified by the authors that the most frequent obstacle of the approach taken by multiple BNN publications to date is the utilization of gradient descent, given that the procedure was originally designed to deal with continuous values, not with discrete spaces. Even when it has been shown that precision reduction (Float32 -> Float16 -> Int16) can train NN at a comparable precision [1], the problem resides in the adaptation of a method originally designed for continuous contexts into a different set of values that create instabilities at time of training.

In order to tackle that problem, it is imperative to take a completely different approach to how BNNs are trained, which is the main proposition of this project, in which we expose a new methodology to obtain neural networks that use binary values in weights, activations, operations and is completely gradient free; which brings us to the brief summary of the capabilities of this implementation:

• Use weights and activations as unsigned short int values (16 bits)
• Use only logic operations (AND, XOR, OR...), no need of Arithmetic Logic Units (ALU)
• Calculate distance between individuals with hamming distance
• Use evolutionary algorithms to drive the space search and network topology updates.

These substantial changes simplify the computing architecture needed to execute the algorithm, which match natively with the Logic Units in the FPGA, but also allows us to design processing elements that effectively adapt to the problem to be solved, while at the same time, remain power efficient in terms of the units needed to deploy because agents with un-optimized structures would automatically be disregarded.

The algorithm proposed, Binary SUNA (SUNA [2] with binary extensions ), will be used to solve standard reinforcement learning challenges, which are going to be connected to an FPGA to solve them more efficiently, given that the architecture will match the evolved network at multiple stages, specially during training and inference. Comparison of the performance gains between CPU, GPU and FPGA will be demonstrated.

[1] Michaela Blott et al. 2018. FINN-R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks. ACM Trans. Reconfigurable Technology
[2] Danilo Vargas et al. 2017. Spectrum-Diverse Neuroevolution With Unified Neural Models. IEEE Transactions on Neural Networks and Learning Systems 28

Project Proposal

1. High-level Project Description

2. Block Diagram

3. Intel FPGA Virtues in Your Project

4. Design Introduction

5. Function Description


6. Performance Parameters

7. Design Architecture


Doreen Liu
excellent idea! The proposal hasn't finished, please upload it soon.The deadline of the first stage is 2019-06-30.
🕒 Jun 26, 2019 11:34 AM

Please login to post a comment.