Works (5)
Updated: April 11th, 2023 10:13
2021 journal article
A Scalable Cluster-based Hierarchical Hardware Accelerator for a Cortically Inspired Algorithm
ACM JOURNAL ON EMERGING TECHNOLOGIES IN COMPUTING SYSTEMS, 17(4).
author keywords: Neuromorphic computing; accelerator; cortical processor; hierarchical temporal memory; sparse distributed memory
TL;DR:
A scalable, configurable and cluster-based hierarchical hardware accelerator through custom hardware architecture for Sparsey, a cortical learning algorithm inspired by the operation of the human cortex that uses a Sparse Distributed Representation to enable unsupervised learning and inference in the same algorithm.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
7. Affordable and Clean Energy
(OpenAlex)
Sources: Web Of Science, NC State University Libraries
Added: February 28, 2022
2021 journal article
Hardware Implementation of Hierarchical Temporal Memory Algorithm
ACM JOURNAL ON EMERGING TECHNOLOGIES IN COMPUTING SYSTEMS, 18(1).
author keywords: Hierarchical temporal memory (HTM); ASIC design; distributed memory; KTH benchmark
TL;DR:
Hierarchical temporal memory is an un-supervised machine learning algorithm that can learn both spatial and temporal information of input that has been successfully applied to multiple areas.
(via Semantic Scholar)
open access via dl.acm.org (publisher PDF)
UN Sustainable Development Goal Categories
11. Sustainable Cities and Communities
(Web of Science)
Source: Web Of Science
Added: January 23, 2023
conference paper
Hardware implementation of hierarchical temporal memory algorithm
Li, W. F., & Franzon, P. 2016 29th IEEE International System-on-Chip Conference (SOCC), 133–138.
Source: NC State University Libraries
Added: August 6, 2018
conference paper
Hardware implementation of hierarchical temporal memory algorithm
Li, W. F., & Franzon, P. 2016 29th IEEE International System-on-Chip Conference (SOCC), 133–138.
Source: NC State University Libraries
Added: August 6, 2018
conference paper
Processor-in-memory support for artificial neural networks
Schabel, J., Baker, L., Dey, S., Li, W. F., & Franzon, P. D. 2016 IEEE International Conference on Rebooting Computing (icrc).
Source: NC State University Libraries
Added: August 6, 2018
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