Deep neural networks (DNN) have demonstrated highly promising results across computer
vision and speech recognition, and are becoming foundational for ubiquitous AI. The …

We present a recurrent neural network (RNN) accelerator design with resistive random-
access memory (ReRAM)-based processing-in-memory (PIM) architecture. Distinguished …

When training deep neural networks (DNNs), expensive floating point arithmetic units are
used in GPUs or custom neural processing units (NPUs). To reduce the burden of floating …

Long Short Term Memory (LSTM) networks are a class of recurrent neural networks that are
widely used for machine learning tasks involving sequences, including machine translation …

This paper presents an approximate computing method of long short-term memory (LSTM)
operations for energy-efficient end-to-end speech recognition. We newly introduce the …

This paper proposes that approximation by reducing bit-precision and using inexact
multiplier can save power consumption of digital multilayer perceptron accelerator during …

Neural networks generally require significant memory capacity/bandwidth to store/access a
large number of synaptic weights. This paper presents an application of JPEG image …

This paper presents a novel stacking and multi-level indexing scheme for convolutional
neural networks (CNNs) used in energy-limited edge-level systems. Basically, the proposed …

Modern deep learning systems rely on (a) a hand-tuned neural network topology,(b)
massive amounts of labeled training data, and (c) extensive training over large-scale …

Neural networks generally require significant memory capacity/bandwidth to store/access a
large number of synaptic weights. This paper presents design of an energy-efficient neural …