Emergent properties have been widely adopted as a term to describe behavior not present
in smaller models but observed in larger models (Wei et al., 2022a). Recent work suggests …

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 …

The likelihood of encountering in-training failures rises substantially with larger Deep
Learning (DL) training workloads, leading to lost work and resource wastage. Such failures …

In recent years, PIM-based mixed-signal accelerators have been proposed as energy-and
area-efficient solutions with ultra-high throughput to accelerate DNN computations …

With the increase in the scale of Deep Learning (DL) training workloads in terms of compute
resources and time consumption, the likelihood of encountering in-training failures rises …

To enable efficient computation for convolutional neural networks, in-memory-computing
(IMC) is proposed to perform computation within memory. However, the non-ideality …

In recent years, processing in memory (PIM) based mixedsignal designs have been
proposed as energy-and area-efficient solutions with ultra high throughput to accelerate …

A key challenge for deployment of artificial intelligence (AI) in real-time safety-critical
systems at the edge is to ensure reliable performance even in unreliable environments. This …

In-memory computing (IMC) has become the current trend to accelerate the inference of
deep neural networks (DNNs). Nonetheless, IMC suffers from variations that significantly …

Transformer-based language models have shown outstanding performance in various NLP
tasks, but using them on edge devices is very challenging due to their notorious memory …