Scale-CIM: Precision-scalable computing-in-memory for energy-efficient quantized neural networks
Quantized neural networks (QNNs), which perform multiply-accumulate (MAC) operations
with low-precision weights or activations, have been widely exploited to reduce energy …
with low-precision weights or activations, have been widely exploited to reduce energy …
Near-Memory Computing With Compressed Embedding Table for Personalized Recommendation
Deep learning (DL)-based recommendation models play an important role in many real-
world applications. However, an embedding layer, which is a key part of the DL-based …
world applications. However, an embedding layer, which is a key part of the DL-based …
Accelerating DNA Sequence Analysis using content-addressable memory in FPGAs
Biological sequence alignment is a widely used technique where the sequence databases
are searched to find similar sequence to the input query. In this work we focus on the most …
are searched to find similar sequence to the input query. In this work we focus on the most …
A ReRAM-based Nonvolatile PIM
This work presents a design and analysis of a resistive random-access memory (ReRAM)-
based nonvolatile processor-in-memory (nvPIM). The nvPIM successfully demonstrates …
based nonvolatile processor-in-memory (nvPIM). The nvPIM successfully demonstrates …
Accelerating Neural Network Training with Processing-in-Memory GPU
Processing-in-memory (PIM) architecture is promising for accelerating deep neural network
(DNN) training due to its low-latency and energy-efficient data movement between …
(DNN) training due to its low-latency and energy-efficient data movement between …