Sumon Dey

Works (8)

Updated: April 11th, 2023 10:13

2022 article proceedings

FAXID: FPGA-Accelerated XGBoost Inference for Data Centers using HLS

By: A. Gajjar n, P. Kashyap n , A. Aysu n , P. Franzon n , S. Dey* & C. Cheng *

topics (OpenAlex): Advanced Malware Detection Techniques; Adversarial Robustness in Machine Learning; Advanced Neural Network Applications
TL;DR: An FPGA-based XGBoost accelerator designed with High-Level Synthesis (HLS) tools and design flow accelerating binary classification inference is showcased, showing a latency speedup of the proposed design over state-of-art CPU and GPU implementations, including energy efficiency and cost-effectiveness. (via Semantic Scholar)
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7. Affordable and Clean Energy (OpenAlex)
Sources: Web Of Science, NC State University Libraries, ORCID, Crossref
Added: October 11, 2022

2022 article proceedings

Modeling of Adaptive Receiver Performance Using Generative Adversarial Networks

By: P. Kashyap n , Y. Choi *, S. Dey*, D. Baron n , C. Wong n , T. Wu n , C. Cheng *, P. Franzon n 

author keywords: SerDes; receiver; behavior modeling; adaptive; generative; GAN; DFE; IBIS-AMI
topics (OpenAlex): Image and Signal Denoising Methods; Speech and Audio Processing; Advanced Image Processing Techniques
TL;DR: A data-driven approach to modeling a high-speed serializer/deserializer (SerDes) receiver through generative adversarial networks (GANs) through the use of a discriminator structure that improves the training to generate a contour plot that makes it difficult to distinguish the ground truth. (via Semantic Scholar)
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10. Reduced Inequalities (OpenAlex)
Sources: Web Of Science, NC State University Libraries, ORCID, Crossref
Added: September 19, 2022

2021 article

A Scalable Cluster-based Hierarchical Hardware Accelerator for a Cortically Inspired Algorithm

Dey, S., Baker, L., Schabel, J., Li, W., & Franzon, P. D. (2021, June 30). ACM Journal on Emerging Technologies in Computing Systems, Vol. 10.

By: S. Dey n, L. Baker n, J. Schabel  n, W. Li n & P. Franzon n 

author keywords: Neuromorphic computing; accelerator; cortical processor; hierarchical temporal memory; sparse distributed memory
topics (OpenAlex): Advanced Memory and Neural Computing; Advanced Neural Network Applications; Stochastic Gradient Optimization Techniques
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 Goals Color Wheel
UN Sustainable Development Goal Categories
7. Affordable and Clean Energy (OpenAlex)
Sources: Web Of Science, NC State University Libraries
Added: February 28, 2022

2021 article proceedings

Design for 3D Stacked Circuits

By: P. Franzon n , W. Davis n, E. Rotenberg n , J. Stevens n, S. Lipa n, T. Nigussie n, H. Pan  n, L. Baker n ...

topics (OpenAlex): 3D IC and TSV technologies; Semiconductor materials and devices; Interconnection Networks and Systems
TL;DR: 2.5D and 3D technologies can give rise to a node equivalent of scaling due to improved connectivity because of improved connectivity, but design issues that need to be addressed in pursuing such exploitations include thermal management, design for test and computer aided design. (via Semantic Scholar)
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Sources: Web Of Science, NC State University Libraries, Crossref
Added: July 11, 2022

2021 article

Hardware Implementation of Hierarchical Temporal Memory Algorithm

Li, W., Franzon, P., Dey, S., & Schabel, J. (2021, October 22). ACM Journal on Emerging Technologies in Computing Systems, Vol. 18.

By: W. Li n, P. Franzon n , S. Dey n & J. Schabel  n

author keywords: Hierarchical temporal memory (HTM); ASIC design; distributed memory; KTH benchmark
topics (OpenAlex): Advanced Data Compression Techniques; Network Packet Processing and Optimization; Graph Theory and Algorithms
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)
UN Sustainable Development Goals Color Wheel
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Sources: Web Of Science, NC State University Libraries
Added: January 23, 2023

conference paper

Design and ASIC acceleration of cortical algorithm for text recognition

Dey, S., & Franzon, P. D. 2016 29th IEEE International System-on-Chip Conference (SOCC), 114–119.

By: S. Dey & P. Franzon 

Source: NC State University Libraries
Added: August 6, 2018

conference paper

Design and ASIC acceleration of cortical algorithm for text recognition

Dey, S., & Franzon, P. D. 2016 29th IEEE International System-on-Chip Conference (SOCC), 114–119.

By: S. Dey & P. Franzon 

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).

By: J. Schabel, L. Baker, S. Dey, W. Li & P. Franzon 

Source: NC State University Libraries
Added: August 6, 2018

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