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Nathaniel Pinckney

Nathaniel Pinckney received his PhD from David Blaauw’s research group at the University of Michigan in 2015. His research focused on near-threshold characterization of planar and FinFET devices, and fast voltage boosting. Prior to UM he worked for two years in Sun Microsystems’ VLSI Research group (presently Oracle Labs). His undergraduate degree is from Harvey Mudd College, where he was advised by David Money Harris.

Main Field of Interest: 
Circuits and VLSI Design
Additional Research Areas: 
Computer Architecture
Artificial Intelligence and Machine Learning
Google Scholar: 
https://scholar.google.com/citations

Publications

A 0.32–128 TOPS, Scalable Multi-Chip-Module-Based Deep Neural Network Inference Accelerator With Ground-Referenced Signaling in 16 nm
MAGNet: A Modular Accelerator Generator for Neural Networks
Simba: Scaling Deep-Learning Inference withMulti-Chip-Module-Based Architecture
A 0.11 pJ/Op, 0.32-128 TOPS, Scalable Multi-Chip-Module-based Deep Neural Network Accelerator Designed with a High-Productivity VLSI Methodology
A 0.11 pJ/Op, 0.32-128 TOPS, Scalable Multi-Chip-Module-based Deep Neural Network Accelerator with Ground-Reference Signaling in 16nm
A Fine-Grained GALS SoC with Pausible Adaptive Clocking in 16 nm FinFET
A Modular Digital VLSI Flow for High-Productivity SoC Design
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