COIN: Combinational Intelligent Networks
Igor D. S. Miranda, Aman Arora, Zachary Susskind, Josias S. A. Souza, Mugdha Jadhao, Luis A. Q. Villon, Diego Leonel Cadette Dutra, Priscila M. V. Lima, Felipe M. G. França, Maurício Breternitz, Lizy K. John · 2023
We introduce Combinational Intelligent Networks (COIN), a machine learning technique that targets edge inference using low-resourced FPGAs or ASICs. COIN is an improvement on LogicWiSARD, a recent weightless neural network that achieves low power, small area, and high throughput. We convert the LogicWiSARD model into a binary neural network, train it using backpropagation, and then convert it to a COIN model. As a result, COIN can achieve higher accuracy than LogicWiSARD or it can require significantly fewer hardware resources when comparing models with similar accuracies. In comparison to a BNN implementation, FINN, small and large COIN models are more energy efficient demonstrating up to 11.5x higher inferences/Joule at similar accuracy. Our tool executes the complete flow, from training to RTL. and is publicly available.