# ChewBaccaNN: A Flexible 223 TOPS/W BNN Accelerator

arXiv: https://arxiv.org/abs/2005.07137 · HTML: https://mlsanity.com/paper/2005.07137/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2020.

## Built on by (most cited first, 9 of 9)

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- [BrainTTA: A 35 fJ/op Compiler Programmable Mixed-Precision Transport-Triggered NN SoC](https://mlsanity.com/paper/2211.11331/) (2022)
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- [Low- and Mixed-Precision Inference Accelerators](https://mlsanity.com/paper/2206.12358/) (2022)
- [Attack on a PUF-based Secure Binary Neural Network](https://mlsanity.com/paper/2510.24422/) (2025)

## Abstract (arXiv)

Binary Neural Networks enable smart IoT devices, as they significantly reduce the required memory footprint and computational complexity while retaining a high network performance and flexibility. This paper presents ChewBaccaNN, a 0.7 mm2 sized binary convolutional neural network (CNN) accelerator designed in GlobalFoundries 22 nm technology. By exploiting efficient data re-use, data buffering, latch-based memories, and voltage scaling, a throughput of 241 GOPS is achieved while consuming just 1.1 mW at 0.4V/154MHz during inference of binary CNNs with up to 7x7 kernels, leading to a peak core energy efficiency of 223 TOPS/W. ChewBaccaNN's flexibility allows to run a much wider range of binary CNNs than other accelerators, drastically improving the accuracy-energy trade-off beyond what can be captured by the TOPS/W metric. In fact, it can perform CIFAR-10 inference at 86.8% accuracy with merely 1.3 μ J, thus exceeding the accuracy while at the same time lowering the energy cost by 2.8x compared to even the most efficient and much larger analog processing-in-memory devices, while keeping the flexibility of running larger CNNs for higher accuracy when needed. It also runs a binary ResNet-18 trained on the 1000-class ILSVRC dataset and improves the energy efficiency by 4.4x over accelerators of similar flexibility. Furthermore, it can perform inference on a binarized ResNet-18 trained with 8-bases Group-Net to achieve a 67.5% Top-1 accuracy with only 3.0 mJ/frame -- at an accuracy drop of merely 1.8% from the full-precision ResNet-18.