Several manufacturers have already started to commercialize near-bank Processing-In-
Memory (PIM) architectures, after decades of research efforts. Near-bank PIM architectures …

Quantization is a technique to reduce the computation and memory cost of DNN models,
which are getting increasingly large. Existing quantization solutions use fixed-point integer …

As its core computation, a self-attention mechanism gauges pairwise correlations across the
entire input sequence. Despite favorable performance, calculating pairwise correlations is …

Specialized accelerators have recently garnered attention as a method to reduce the power
consumption of neural network inference. A promising category of accelerators utilizes …

Nanopore sequencing is a widely-used high-throughput genome sequencing technology
that can sequence long fragments of a genome into raw electrical signals at low cost …

Graph Neural Networks (GCNs) have attracted wide attention and are applied to the real
world. However, due to the ever-growing graph data with significant irregularities, off-chip …

Resistive random-access memory (ReRAM)-based manycore architectures enable
acceleration of graph neural network (GNN) inference and training. GNNs exhibit …

Graph analytics, which mainly includes graph processing, graph mining, and graph learning,
has become increasingly important in several domains, including social network analysis …

Processing-in-Memory (PIM) has been actively studied to overcome the memory bottleneck
by placing computing units near or in memory, especially for efficiently processing low …

Weight quantization is well adapted to cope with the ever-growing complexity of the deep
neural network (DNN) model. Diversified quantization schemes lead to diverse quantized bit …