The unprecedented progress in artificial intelligence (AI), particularly in deep learning
algorithms with ubiquitous internet connected smart devices, has created a high demand for …

In-memory computing seeks to minimize data movement and alleviate the memory wall by
computing in-situ, in the same place that the data is located. One of the key emerging …

Data movement between the main memory and the processor is a key contributor to
execution time and energy consumption in memory-intensive applications. This data …

This work presents an associative in-memory deep learning processor (AIDA) for edge
devices. An associative processor is a massively parallel non-von Neumann accelerator that …

Database analytic query workloads are heavy consumers of data-center cycles, and there is
constant demand to improve their performance. Associative processors (AP) have re …

In this paper, we propose an energy-efficient, reliable, hybrid, 10-transistor/2-Double-Barrier-
Magnetic-Tunnel-Junction (10T2DMTJ) non-volatile (NV) ternary content-addressable …

While extensive research has focused on optimizing performance and efficiency in vision-
based AI accelerators, an unexplored phenomenon, Clustering Similarity Effect, presents a …

There has been a resurgence of interest in vector architectures evident by recent adoption of
vector extensions in mainstream instruction set architectures. Traditionally, vector engines …

Content-addressable memory (CAM) is a specialized type of memory that facilitates
massively parallel comparison of a search pattern against its entire content. State-of-the-art …

Non-volatile memory (NVM) crossbars have been identified as a promising technology, for
accelerating important machine learning operations, with matrix-vector multiplication being a …