This paper proposes a hybrid deep neural network (DNN) for automatic speech recognition
and an energy-efficient reconfigurable architecture with approximate computing for …

An ultra-low power always-on keyword spotting (KWS) accelerator is implemented in 22nm
CMOS technology, which is based on an optimized convolutional neural network (CNN). To …

Abstract Convolution Neural Networks are now widely used in image processing, object
detection, video detection, and other classification tasks. Thus the acceleration of CNN is …

This paper proposed an energy-efficient reconfigurable accelerator for keyword spotting
(EERA-KWS) based on binary weight network (BWN) and fabricated in 28-nm CMOS …

Convolutional Neural Networks (CNNs) have been widely used in many computer
applications. The growth in deep neural networks and machine learning applications has …

Recently, due to the immense growth of computing power, image processing and
Convolutional neural networks (CNN) have regained gigantic attention because of the …

This paper proposes a background noise adaptive energy-efficient keywords recognition
processor with Reusable DNN (RDNN) and reconfigurable architecture. To reduce power …

In this paper, an ultra-low power speech recognition processor is implemented based on an
optimized binarized weight neural-network (BWN). To accelerate the BWN and make it …

This paper proposes an energy-efficient reconfigurable architecture for deep neural
networks (EERA-DNN) with hybrid bit-width and logarithmic multiplier. To speed up the …

Approximate computing is a promising approach to reduce power consumption in error-
resilient applications with lax precision constraints. This paper presents an energy-efficient …