Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted
in breakthroughs in many areas. However, deploying these highly accurate models for data …

Deep Neural Networks (DNNs) are very popular because of their high performance in
various cognitive tasks in Machine Learning (ML). Recent advancements in DNNs have …

The state-of-the-art hardware platforms for training deep neural networks are moving from
traditional single precision (32-bit) computations towards 16 bits of precision-in large part …

In this paper, we propose a number of novel techniques and numerical representation
formats that enable, for the very first time, the precision of training systems to be aggressively …

IMC has the potential to address a critical and foundational challenge affecting computing
platforms today-that is, the high energy and delay costs of moving data and accessing data …

In-memory computing (IMC) has emerged as a new computing paradigm able to alleviate or
suppress the memory bottleneck, which is the major concern for energy efficiency and …

Computing systems at all scales (from mobile handheld devices to supercomputers, servers,
and large cloud-based data centers) have seen significant performance gains, mostly …

Attention-based models have demonstrated remarkable success in various natural
language understanding tasks. However, efficient execution remains a challenge for these …

Deep learning algorithms achieve high classification accuracy at the expense of significant
computation cost. In order to reduce this cost, several quantization schemes have gained …

Computing systems are conventionally designed to operate as accurately as possible.
However, this trend faces severe technology challenges, such as power consumption, circuit …