Low Energy Sketching Engines on Many-Core Platform for Big Data Acceleration

Author:

Kulkarni Amey1,Abtahi Tahmid1,Smith Emily1,Mohsenin Tinoosh1

Affiliation:

1. University of Maryland Baltimore County, Baltimore, MD, USA

Funder

National Science Foundation

Publisher

ACM

Reference15 articles.

1. M. Andrecut. Fast gpu implementation of sparse signal recovery from random projections 2008. M. Andrecut. Fast gpu implementation of sparse signal recovery from random projections 2008.

2. Gradient descent with sparsification

3. Energy-efficient mapping of biomedical applications on domain-specific accelerator under process variation

4. Sketching-based high-performance biomedical big data processing accelerator

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1. SENSE: Sketching Framework for Big Data Acceleration on Low Power Embedded Cores;Internet of Things;2018-12-14

2. BiGNoC: Accelerating Big Data Computing with Application-Specific Photonic Network-on-Chip Architectures;IEEE Transactions on Parallel and Distributed Systems;2018-11-01

3. Accelerating Convolutional Neural Network With FFT on Embedded Hardware;IEEE Transactions on Very Large Scale Integration (VLSI) Systems;2018-09

4. MC3A;Proceedings of the 2018 on Great Lakes Symposium on VLSI;2018-05-30

5. Low Overhead CS-Based Heterogeneous Framework for Big Data Acceleration;ACM Transactions on Embedded Computing Systems;2018-01-12

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