Tsetlin Machine-Based Image Classification FPGA Accelerator With On-Device Training

The Tsetlin Machine (TM) is a novel machine learning algorithm that uses Tsetlin automata (TAs) to define propositional logic expressions (clauses) for classification. This paper describes a field-programmable gate array (FPGA) accelerator for image classification based on the Convolutional Coalesced Tsetlin Machine. The accelerator classifies booleanized images of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$28\times 28$ </tex-math></inline-formula> pixels into 10 classes, and is configured with 128 clauses in a highly parallel architecture. To achieve fast clause evaluation and class prediction, the TA action signals and the clause weights per class are available from registers. Full on-device training is included, and the TAs are implemented with 34 Block RAM (BRAM) instances which operate in parallel. Each BRAM is addressed by the clause number and has a 72-bit word width that supports 8 TAs. The design is implemented in a Xilinx Zynq Ultrascale+ XCZU7 FPGA. Running at 50 MHz, the accelerator core achieves 134k image classifications per second, with an energy consumption per classification of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$13.3~\mu $ </tex-math></inline-formula> J. A single training epoch of 60k samples requires a processing time of 1.5 seconds. The accelerator obtains a test accuracy of 97.6% on MNIST, 84.1% on Fashion-MNIST and 82.8% on Kuzushiji-MNIST.