Nonvolatile memory (NVM)-based computing-in-memory (nvCIM) is a promising candidate
for artificial intelligence (AI) edge devices to overcome the latency and energy consumption …

The field of design and design automation of micro-/nano-circuits and systems has played a
pivotal role in advancing information technologies that are an inseparable part of all our …

Implementing edge artificial intelligence (AI) inference and training is challenging with
current memory technologies. As deep neural networks (DNNs) grow in size, this problem is …

Repeated off-chip memory accesses to DRAM drive up operating power for data-intensive
applications, and SRAM technology scaling and leakage power limits the efficiency of …

This article presents an energy-efficient deep neural network (DNN) accelerator with non-
volatile embedded resistive random access memory (RRAM) for mobile machine learning …

This article presents RoboVisio, an efficient and highly flexible domain-specific system-on-
chip (SoC) for vision tasks in fully autonomous micro-robot navigation. A novel hybrid …

Deep neural networks (DNNs) usually have multiple layers and thousands of trainable
parameters to ensure high accuracy. Due to the requirement of large amounts of …

In recent years, we have witnessed explosive growth in machine learning and deep learning-
related applications. In particular, deep artificial neural networks (DNNs) have shown …

With the end of Dennard scaling and the decline of Moore's law, there are no longer
'free'performance and efficiency gains from semiconductor technology advancements …

The unabated pursuit of omniscient and omnipotent AI is levying hefty latency, memory, and
energy taxes at all computing scales. At the same time, the twilight of Dennard scaling …