FPGA Fastfood - A High Speed Systolic Implementation of a Large Scale Online Kernel Method

Abstract

In this paper, we describe a systolic Field Programmable Gate Array (FPGA) implementation of the Fastfood algorithm that is optimised to run at a high frequency. The Fastfood algorithm supports online learning for large scale kernel methods. Empirical results show that 500 MHz clock rates can be sustained for an architecture that can solve problems with input dimensions that are $10^3$ times larger than previously reported. Unlike many recent deep learning publications, this design implements both training and prediction. This enables the use of kernel methods in applications requiring a rare combination of capacity, adaption and speed.