# Weightless Neural Networks for Efficient Edge Inference

arXiv: https://arxiv.org/abs/2203.01479 · HTML: https://mlsanity.com/paper/2203.01479/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 45 references that are in the map.
- First posted on arXiv 3 Mar 2022.

## Builds on (most cited first, 6 of 6)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [A Survey of Quantization Methods for Efficient Neural Network Inference](https://mlsanity.com/paper/2103.13630/) (2021)
- [Efficient Processing of Deep Neural Networks: A Tutorial and Survey](https://mlsanity.com/paper/1703.09039/) (2017)
- [Fast inference of deep neural networks in FPGAs for particle physics](https://mlsanity.com/paper/1804.06913/) (2018)
- [Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1](https://mlsanity.com/paper/1602.02830/) (2016)
- [Certifying Certainty and Uncertainty in Approximate Membership Query Structures -- Extended Version](https://mlsanity.com/paper/2004.13312/) (2020)

## Built on by (most cited first, 13 of 13)

- [KANEL'E: Kolmogorov-Arnold Networks for Efficient LUT-based Evaluation](https://mlsanity.com/paper/2512.12850/) (2025)
- [ULEEN: A Novel Architecture for Ultra Low-Energy Edge Neural Networks](https://mlsanity.com/paper/2304.10618/) (2023)
- [WARP-LUTs -- Walsh-Assisted Relaxation for Probabilistic Look Up Tables](https://mlsanity.com/paper/2510.15655/) (2025)
- [LL-ViT: Edge Deployable Vision Transformers with Look Up Table Neurons](https://mlsanity.com/paper/2511.00812/) (2025)
- [WARP Logic Neural Networks](https://mlsanity.com/paper/2602.03527/) (2026)
- [Polynomial Surrogate Training for Differentiable Ternary Logic Gate Networks](https://mlsanity.com/paper/2603.00302/) (2026)
- [AI Compute Architecture and Evolution Trends](https://mlsanity.com/paper/2508.21394/) (2025)
- [BitLogic: Training Framework for Gradient-Based FPGA-Native Neural Networks](https://mlsanity.com/paper/2602.07400/) (2026)
- [Shrinking the Giant : Quasi-Weightless Transformers for Low Energy Inference](https://mlsanity.com/paper/2411.01818/) (2024)
- [LUNA: LUT-Based Neural Architecture for Fast and Low-Cost Qubit Readout](https://mlsanity.com/paper/2512.07808/) (2025)
- [GLANCE: Gaze-Led Attention Network for Compressed Edge-inference](https://mlsanity.com/paper/2603.15717/) (2026)
- [TsetlinWiSARD: On-Chip Training of Weightless Neural Networks using Tsetlin Automata on FPGAs](https://mlsanity.com/paper/2603.24186/) (2026)
- [Resource Utilization of Differentiable Logic Gate Networks Deployed on FPGAs](https://mlsanity.com/paper/2605.04109/) (2026)

## Abstract (arXiv)

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.