It has been demonstrated that with concrete hardware Trojans, a remote adversary can
mount physical attacks, eg, fault or side-channel attacks, against adjacent IP cores in an …

The last decade has witnessed remarkable research advances at the intersection of
machine learning (ML) and hardware security. The confluence of the two technologies has …

This chapter is on the security assessment of artificial intelligence (AI) and neural network
(NN) accelerators in the face of fault injection attacks. More specifically, it discusses the …

In this chapter, we are considering the physical security of Machine Learning (ML)
implementations on Edge Devices. We list the state-of-the-art known physical attacks, with …

Deep neural network (DNN) accelerators overcome the power and memory walls for
executing neural-net models locally on edge-computing devices to support sophisticated AI …

Specialized acceleration hardware for artificial deep neural network inference is available
from the cloud to the edge. FPGAs in particular are heavily advertised for the acceleration of …

The rapid development of deep learning accelerator has unlocked new applications that
require local inference at the edge device. However, this trend of development to facilitate …