Cargando…

An Approximate GEMM Unit for Energy-Efficient Object Detection

Edge computing brings artificial intelligence algorithms and graphics processing units closer to data sources, making autonomy and energy-efficient processing vital for their design. Approximate computing has emerged as a popular strategy for energy-efficient circuit design, where the challenge is t...

Descripción completa

Detalles Bibliográficos
Autores principales:	Pilipović, Ratko, Risojević, Vladimir, Božič, Janko, Bulić, Patricio, Lotrič, Uroš
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	MDPI 2021
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8234017/ https://www.ncbi.nlm.nih.gov/pubmed/34207295 http://dx.doi.org/10.3390/s21124195

Ejemplares similares

On the Design of an Energy Efficient Digital IIR A-Weighting Filter Using Approximate Multiplication
por: Pilipović, Ratko, et al.
Publicado: (2021)

Accurate Indoor Sound Level Measurement on a Low-Power and Low-Cost Wireless Sensor Node
por: Risojević, Vladimir, et al.
Publicado: (2018)

GEMMs as preclinical models for testing pancreatic cancer therapies
por: Gopinathan, Aarthi, et al.
Publicado: (2015)

Analysis of GEMMs helps unravel genetic heterogeneity in colorectal cancer
Publicado: (2014)

Data Transmission Efficiency in Bluetooth Low Energy Versions
por: Bulić, Patricio, et al.
Publicado: (2019)

GEMME: A Simple and Fast Global Epistatic Model Predicting Mutational Effects
por: Laine, Elodie, et al.
Publicado: (2019)

GEMMs Are a Gem When it Comes to Defining the Role of HIF2α in Mucinous Cystic Neoplasms
por: Boyle, Kathleen A., et al.
Publicado: (2017)

Using Markov Chains and Multi-Objective Optimization for Energy-Efficient Context Recognition †
por: Janko, Vito, et al.
Publicado: (2017)

Heterogeneous computing architecture for fast detection of SNP-SNP interactions
por: Sluga, Davor, et al.
Publicado: (2014)

The Grid Enabled Mass Storage System (GEMMS): the Storage and Data management system used at the INFN Tier1 at CNAF.
por: Ricci, Pier Paolo
Publicado: (2012)

Deep Multi-OMICs and Multi-Tissue Characterization in a Pre- and Postprandial State in Human Volunteers: The GEMM Family Study Research Design
por: Bastarrachea, Raul A., et al.
Publicado: (2018)

Towards precision medicine: defining and characterizing adipose tissue dysfunction to identify early immunometabolic risk in symptom-free adults from the GEMM family study
por: Rodriguez-Ayala, Ernesto, et al.
Publicado: (2020)

Energy-Efficient Object Detection and Tracking Framework for Wireless Sensor Network
por: Dev, Jayashree, et al.
Publicado: (2023)

MBRS-33. TEMPORARY RESTORATION OF p53 ACTIVITY DURING FRACTIONATED RADIOTHERAPY IN A GROUP3 MEDULLOBLASTOMA GEMM REPRESENTS A POWERFUL TOOL FOR RADIOBIOLOGY STUDIES
por: Morcavallo, Alaide, et al.
Publicado: (2020)

Fast Approximation for Sparse Coding with Applications to Object Recognition
por: Sun, Zhenzhen, et al.
Publicado: (2021)

An Approximate Estimate of the Energy Contained in Strychnine
por: Barr, A. D.
Publicado: (1893)

Greedy Approximation
por: Temlyakov, Vladimir
Publicado: (2011)

Efficient Fiducial Point Detection of ECG QRS Complex Based on Polygonal Approximation
por: Lee, Seungmin, et al.
Publicado: (2018)

Lightweight and Energy-Efficient Deep Learning Accelerator for Real-Time Object Detection on Edge Devices
por: Kim, Kyungho, et al.
Publicado: (2023)

Noniterative Doubles Corrections to the Random Phase and Higher Random Phase Approximations: Singlet and Triplet Excitation Energies
por: Haase, Pi A. B., et al.
Publicado: (2019)

A General Framework for Making Context-Recognition Systems More Energy Efficient †
por: Janko, Vito, et al.
Publicado: (2021)

Efficient Fine Tuning for Fashion Object Detection
por: Ma, Benjiang, et al.
Publicado: (2023)

Approximation methods for efficient learning of Bayesian networks
por: Riggelsen, C
Publicado: (2008)

Nuclear distorted waves in high-energy approximation
por: Lukyanov, V K
Publicado: (1994)

High-energy approximation for nucleus-nucleus scattering
por: Lukyanov, V K, et al.
Publicado: (1999)

Rational Approximation and Interpolation : United Kingdom - United States Conference
por: Graves-Morris, Peter, et al.
Publicado: (1984)

Energy-Efficient Hardware Implementation of Fully Connected Artificial Neural Networks Using Approximate Arithmetic Blocks
por: Esmali Nojehdeh, Mohammadreza, et al.
Publicado: (2023)

Spherical harmonics and approximations on the unit sphere: an introduction
por: Atkinson, Kendall, et al.
Publicado: (2012)

Objective Bayesian Inference in Probit Models with Intrinsic Priors Using Variational Approximations
por: Li, Ang, et al.
Publicado: (2020)

Approximate calculation of integrals
por: Krylov, Vladimir Ivanovich, et al.
Publicado: (1962)

Approximate and Renormgroup Symmetries
por: Ibragimov, Nail H, et al.
Publicado: (2009)

Approximate Number Processing Skills Contribute to Decision Making Under Objective Risk: Interactions With Executive Functions and Objective Numeracy
por: Mueller, Silke M., et al.
Publicado: (2018)

Deep Gradient Learning for Efficient Camouflaged Object Detection
por: Ji, Ge-Peng, et al.
Publicado: (2023)

An Efficient Method for Monitoring Birds Based on Object Detection and Multi-Object Tracking Networks
por: Chen, Xian, et al.
Publicado: (2023)

Flexible and Efficient Inference with Particles for the Variational Gaussian Approximation
por: Galy-Fajou, Théo, et al.
Publicado: (2021)

High-energy approximation for on-shell W-pair production
por: Beenakker, W, et al.
Publicado: (1993)

Approximations for W-Pair Production at Linear-Collider Energies
por: Denner, Ansgar, et al.
Publicado: (1997)

Breakdown of the independent particle approximation in high-energy photoionization
por: Dias, E W B, et al.
Publicado: (1997)

On approximation and energy estimates for delta 6-convex functions
por: Saleem, Muhammad Shoaib, et al.
Publicado: (2018)

Sums of four and more unit fractions and approximate parametrizations
por: Elsholtz, Christian, et al.
Publicado: (2021)

Cannot write session to /tmp/vufind_sessions/sess_7s4c6f3hp61o8o88p4htlfqea0