  Weightless Neural Networks (WNNs) are a class of machine learning model which
use table lookups to perform inference. This is in contrast with Deep Neural
Networks (DNNs), which use multiply-accumulate operations. State-of-the-art WNN
architectures have a fraction of the implementation cost of DNNs, but still lag
behind them on accuracy for common image recognition tasks. Additionally, many
existing WNN architectures suffer from high memory requirements. In this paper,
we propose a novel WNN architecture, BTHOWeN, with key algorithmic and
architectural improvements over prior work, namely counting Bloom filters,
hardware-friendly hashing, and Gaussian-based nonlinear thermometer encodings
to improve model accuracy and reduce area and energy consumption. BTHOWeN
targets the large and growing edge computing sector by providing superior
latency and energy efficiency to comparable quantized DNNs. Compared to
state-of-the-art WNNs across nine classification datasets, BTHOWeN on average
reduces error by more than than 40% and model size by more than 50%. We then
demonstrate the viability of the BTHOWeN architecture by presenting an
FPGA-based accelerator, and compare its latency and resource usage against
similarly accurate quantized DNN accelerators, including Multi-Layer Perceptron
(MLP) and convolutional models. The proposed BTHOWeN models consume almost 80%
less energy than the MLP models, with nearly 85% reduction in latency. In our
quest for efficient ML on the edge, WNNs are clearly deserving of additional
attention.
