Approximate computing survey, Part II: Application-specific & architectural approximation techniques and applications
The challenging deployment of compute-intensive applications from domains such as
Artificial Intelligence (AI) and Digital Signal Processing (DSP), forces the community of …
Artificial Intelligence (AI) and Digital Signal Processing (DSP), forces the community of …
Approximate computing: A survey of recent trends—bringing greenness to computing and communication
Energy-efficient computing is a much needed technological advantage for future.
Approximate or inexact computing is a computing paradigm that can trade energy and …
Approximate or inexact computing is a computing paradigm that can trade energy and …
Evoapprox8b: Library of approximate adders and multipliers for circuit design and benchmarking of approximation methods
Approximate circuits and approximate circuit design methodologies attracted a significant
attention of researchers as well as industry in recent years. In order to accelerate the …
attention of researchers as well as industry in recent years. In order to accelerate the …
A retrospective and prospective view of approximate computing [point of view
W Liu, F Lombardi, M Shulte - Proceedings of the IEEE, 2020 - ieeexplore.ieee.org
Computing systems are conventionally designed to operate as accurately as possible.
However, this trend faces severe technology challenges, such as power consumption, circuit …
However, this trend faces severe technology challenges, such as power consumption, circuit …
Revisiting hyperdimensional learning for fpga and low-power architectures
Today's applications are using machine learning algorithms to analyze the data collected
from a swarm of devices on the Internet of Things (IoT). However, most existing learning …
from a swarm of devices on the Internet of Things (IoT). However, most existing learning …
DeepMaker: A multi-objective optimization framework for deep neural networks in embedded systems
Abstract Deep Neural Networks (DNNs) are compute-intensive learning models with
growing applicability in a wide range of domains. Due to their computational complexity …
growing applicability in a wide range of domains. Due to their computational complexity …
Approximate designs for fast Fourier transform (FFT) with application to speech recognition
This paper presents different approximate designs for computing the FFT. The tradeoff
between accuracy and performance is achieved by adjusting the word length in each …
between accuracy and performance is achieved by adjusting the word length in each …
MIMDRAM: An End-to-End Processing-Using-DRAM System for High-Throughput, Energy-Efficient and Programmer-Transparent Multiple-Instruction Multiple-Data …
Processing-using-DRAM (PUD) is a processing-in-memory (PIM) approach that uses a
DRAM array's massive internal parallelism to execute very-wide (eg, 16,384-262,144-bit …
DRAM array's massive internal parallelism to execute very-wide (eg, 16,384-262,144-bit …
Software development lifecycle for energy efficiency: techniques and tools
Motivation: In modern it systems, the increasing demand for computational power is tightly
coupled with ever higher energy consumption. Traditionally, energy efficiency research has …
coupled with ever higher energy consumption. Traditionally, energy efficiency research has …
Approximate computing: Concepts, architectures, challenges, applications, and future directions
AM Dalloo, AJ Humaidi, AK Al Mhdawi… - IEEE …, 2024 - ieeexplore.ieee.org
The unprecedented progress in computational technologies led to a substantial proliferation
of artificial intelligence applications, notably in the era of big data and IoT devices. In the …
of artificial intelligence applications, notably in the era of big data and IoT devices. In the …