Neural computing, guided by brain-inspired computational frameworks, promises to realize
various cognitive and perception-related tasks. Complementary metal–oxide–semiconductor …

Conventional von Neumann architecture suffers from bottlenecks in computing performance
and power consumption due to frequent data exchange between memory and processing …

This article presents an overview of circuit-level techniques used for approximate computing
(AC), including both computation and data storage units. After providing some background …

We demonstrate the design of efficient and high-performance artificial intelligence (AI)/deep
learning accelerators with customized spin transfer torque (STT)-MRAM (STT-MRAM) and a …

Modern computing platforms expend significant amounts of time and energy in transmitting
data across on-chip and off-chip interconnects. This challenge is exacerbated in prevalent …

Memory subsystems are a major energy bottleneck in computing platforms due to frequent
transfers between processors and off-chip memory. We propose approximate memory …

Embedding Machine Learning enables integrating intelligence in recent application
domains such as Internet of Things, portable healthcare systems, and wearable devices …

Much research has shown that applications have variable runtime cache requirements. In
the context of the increasingly popular spin-transfer torque RAM (STT-RAM) cache, the …

Memory subsystems are a major energy bottleneck in computing platforms due to frequent
transfers between processors and off-chip memory. We propose approximate memory …

Existing memory approximation techniques focus on employing approximations at an
individual level of the memory hierarchy (eg, cache, scratchpad, or main memory). However …