  The recent breakthroughs of deep neural networks (DNNs) and the advent of
billions of Internet of Things (IoT) devices have excited an explosive demand
for intelligent IoT devices equipped with domain-specific DNN accelerators.
However, the deployment of DNN accelerator enabled intelligent functionality
into real-world IoT devices still remains particularly challenging. First,
powerful DNNs often come at prohibitive complexities, whereas IoT devices often
suffer from stringent resource constraints. Second, while DNNs are vulnerable
to adversarial attacks especially on IoT devices exposed to complex real-world
environments, many IoT applications require strict security. Existing DNN
accelerators mostly tackle only one of the two aforementioned challenges (i.e.,
efficiency or adversarial robustness) while neglecting or even sacrificing the
other. To this end, we propose a 2-in-1 Accelerator, an integrated
algorithm-accelerator co-design framework aiming at winning both the
adversarial robustness and efficiency of DNN accelerators. Specifically, we
first propose a Random Precision Switch (RPS) algorithm that can effectively
defend DNNs against adversarial attacks by enabling random DNN quantization as
an in-situ model switch. Furthermore, we propose a new precision-scalable
accelerator featuring (1) a new precision-scalable MAC unit architecture which
spatially tiles the temporal MAC units to boost both the achievable efficiency
and flexibility and (2) a systematically optimized dataflow that is searched by
our generic accelerator optimizer. Extensive experiments and ablation studies
validate that our 2-in-1 Accelerator can not only aggressively boost both the
adversarial robustness and efficiency of DNN accelerators under various
attacks, but also naturally support instantaneous robustness-efficiency
trade-offs adapting to varied resources without the necessity of DNN
retraining.

Machine Learning;Hardware Architecture;Cryptography and Security