# Bit-serial Weight Pools: Compression and Arbitrary Precision Execution of Neural Networks on Resource Constrained Processors

arXiv: https://arxiv.org/abs/2201.11651 · HTML: https://mlsanity.com/paper/2201.11651/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 22 references that are in the map.
- First posted on arXiv 25 Jan 2022.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [PACT: Parameterized Clipping Activation for Quantized Neural Networks](https://mlsanity.com/paper/1805.06085/) (2018)
- [MLPerf Tiny Benchmark](https://mlsanity.com/paper/2106.07597/) (2021)
- [CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs](https://mlsanity.com/paper/1801.06601/) (2018)
- [Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Networks](https://mlsanity.com/paper/1712.01507/) (2017)
- [Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions](https://mlsanity.com/paper/1806.09228/) (2018)
- [A Quantization-Friendly Separable Convolution for MobileNets](https://mlsanity.com/paper/1803.08607/) (2018)
- [Do All MobileNets Quantize Poorly? Gaining Insights into the Effect of Quantization on Depthwise Separable Convolutional Networks Through the Eyes of Multi-scale Distributional Dynamics](https://mlsanity.com/paper/2104.11849/) (2021)
- [Automating Generation of Low Precision Deep Learning Operators](https://mlsanity.com/paper/1810.11066/) (2018)
- [SWIS -- Shared Weight bIt Sparsity for Efficient Neural Network Acceleration](https://mlsanity.com/paper/2103.01308/) (2021)

## Built on by (most cited first, 3 of 3)

- [FlexiBit: Fully Flexible Precision Bit-parallel Accelerator Architecture for Arbitrary Mixed Precision AI](https://mlsanity.com/paper/2411.18065/) (2024)
- [Towards Efficient and Accurate Spiking Neural Networks via Adaptive Bit Allocation](https://mlsanity.com/paper/2506.23717/) (2025)
- [Deep Microcompression: Structured Pruning and Bit-packed Quantization for Microcontrollers](https://mlsanity.com/paper/2609.05081/) (2026)

## Abstract (arXiv)

Applications of neural networks on edge systems have proliferated in recent years but the ever-increasing model size makes neural networks not able to deploy on resource-constrained microcontrollers efficiently. We propose bit-serial weight pools, an end-to-end framework that includes network compression and acceleration of arbitrary sub-byte precision. The framework can achieve up to 8x compression compared to 8-bit networks by sharing a pool of weights across the entire network. We further propose a bit-serial lookup based software implementation that allows runtime-bitwidth tradeoff and is able to achieve more than 2.8x speedup and 7.5x storage compression compared to 8-bit weight pool networks, with less than 1% accuracy drop.