Abstract Processing-in-memory (PIM) techniques have gained much attention from computer
architecture researchers, and significant research effort has been invested in exploring and …

Due to the amount of data involved in emerging deep learning and big data applications,
operations related to data movement have quickly become a bottleneck. Data-centric …

Silicon photonics (SiPh) technology has facilitated the deployment of integrated photonics
across different application domains, from ultra-fast communication in Datacom applications …

Phase Change Memory (PCM) is an attractive candidate for main memory, as it offers non-
volatility and zero leakage power while providing higher cell densities, longer data retention …

Recent advances in machine learning (ML) have spotlighted the pressing need for
computing architectures that bridge the gap between memory bandwidth and processing …

The integration of silicon photonics (SiPh) and phase change materials (PCMs) has created
a unique opportunity to realize adaptable and reconfigurable photonic systems. In particular …

Recently, phase-change memory (PCM) has emerged as a promising memory and storage
technology. By storing multiple bits in a PCM cell, multi-level-cell (MLC) PCM further reduces …

The integration of phase change materials (PCMs) with photonic devices creates a unique
opportunity for realizing application-specific, reconfigurable, and energy-efficient photonic …

Traditional DRAM-based main memory systems face several challenges with memory
refresh overhead, high latency, and low throughput as the industry moves towards smaller …

Stochastic computing based Processing-In-Memory (PIM) architectures (eg,[1]) can provide
massive parallelism with higher energy-efficiency, for implementing complex computations …