ChewBaccaNN: A flexible 223 TOPS/W BNN accelerator
Renzo Andri, Geethan Karunaratne, Lukas Cavigelli, Luca Benini
Abstract
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 7×7 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 tradeoff 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 μJ, thus exceeding the accuracy while at the same time lowering the energy cost by 2.8× 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 4.4× 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.