Hardware realization of artificial intelligence (AI) requires new design styles and even
underlying technologies than those used in traditional digital processors or logic circuits …

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

Modern computing systems are overwhelmingly designed to move data to computation. This
design choice goes directly against at least three key trends in computing that cause …

Processing-using-DRAM (PUD) is a processing-in-memory (PIM) approach that uses a
DRAM array's massive internal parallelism to execute very-wide (eg, 16,384-262,144-bit …

Training machine learning (ML) algorithms is a computationally intensive process, which is
frequently memory-bound due to repeatedly accessing large training datasets. As a result …

Commodity DRAM-based processing-using-memory (PuM) techniques that are supported
by off-the-shelf DRAM chips present an opportunity for alleviating the data movement …

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 …

For many decades, progress in computing hardware has been closely associated with
CMOS logic density, performance, and cost. As such, slowdown in 2-D scaling, frequency …

Reinforcement Learning (RL) is the process by which an agent learns optimal behavior
through interactions with experience datasets, all of which aim to maximize the reward …

Motivation Recent advances in sequencing technologies have stressed the critical role of
sequence analysis algorithms and tools in genomics and healthcare research. In particular …