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ChewBaccaNN: A Flexible 223 TOPS/W BNN Accelerator by Andri, Renzo; Karunaratne, Geethan; Cavigelli, Lukas; Benini, Luca is a scholarly article available to read on EtoBox.

What is ChewBaccaNN: A Flexible 223 TOPS/W BNN Accelerator about?

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 mm$^2$ 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 $\mu J$, 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 bi

Author

Andri, Renzo; Karunaratne, Geethan; Cavigelli, Lukas; Benini, Luca

Published

2020

Language

EN