Background Deep neural networks, and in particular convolutional neural networks, are being used with increasing frequency for a number of tasks such as image classification, image clustering, and object recognition. In a forward propagation of a conventional convolutional neural network, a kernel is passed over one or more tensors to produce one or...

­Background Deep neural networks (DNNs) have been very successful in large-scale recognition tasks, but they exhibit large computation and memory requirements. To address the memory bottleneck of digital DNN hardware accelerators, in-memory computing (IMC) designs have been presented to perform analog DNN computations inside the memory. Recent IMC...

­Background In the era of artificial intelligence, various deep neural networks (DNNs), such as multi-layer perceptron, convolutional neural networks, and recurrent neural networks, have emerged and achieved human-level performance in many recognition tasks. These DNNs usually require billions of multiply-and-accumulate (MAC) operations, soliciting...

Background Traditional hardware designs for device authentication and secret key generation typically employ physical unclonable functions (PUFs) which generate unique random numbers based on static random-access memory (SRAM), delay, or analog circuit elements. Although silicon PUFs can be highly stable and unique, they do not represent liveliness....

Devices using biometric authentication such as fingerprint scanning lack liveness checks, resulting in devices that are vulnerable to spoofing and allow access to sensitive personal information. Using electrocardiographic (ECG) signals is an alternative to fingerprint scanning because ECG scanning has intrinsic liveness detection that provides increased...