Hai Li

Clare Boothe Luce Professor

Duke University

United States

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About

Hai Li is the Clare Boothe Luce Professor at Duke University in the United States. His research focuses on advanced computing mechanisms, particularly in the fields of AI hardware, in-memory computing, and circuit design. Recent publications include work on epileptic seizure classification, content-addressable memory circuits, and efficient AI hardware co-design. He is also actively involved in exploring near-data processing systems and neuro-symbolic computing challenges.

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2022

Collaborators (8)

Chun Jason Xue

UNITED ARAB EMIRATES

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Duke University

UNITED STATES

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Keshab K. Parhi

University of Minnesota

UNITED STATES

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Hai Li

Research Interests

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About

Articles (21)

MulPi: A Multi-class and Patient-Independent Epileptic Seizure Classifier With Co-Designed Input-stationary Computing-in-SRAM

Advancements in Content-Addressable Memory (CAM) Circuits: State-of-the-Art, Applications, and Future Directions in the AI Domain

Circuits to Systems: Co-Designing Efficient AI Hardware

Emerging Computing Mechanisms for Edge AI

Neural architecture search for in-memory computing-based deep learning accelerators

NDRec: A Near-Data Processing System for Training Large-Scale Recommendation Models

Neuro-Symbolic Computing: Advancements and Challenges in Hardware–Software Co-Design

Editorial: Special Issue for the 75th Anniversary of the IEEE Circuits and Systems Society [Editorial]

Recap of the 61st ACM/IEEE Design Automation Conference (DAC61): The “Chips to Systems Conference”

TFSRAM: A 249.8TOPS/W Timing-to-First-Spike Compute-in-Memory Neuromorphic Processing Engine With Twin-Column SRAM Synapses

EMS-i : An Efficient Memory System Design with Specialized Caching Mechanism for Recommendation Inference

SpikeSen: Low-Latency In-Sensor-Intelligence Design With Neuromorphic Spiking Neurons

DyNNamic: Dynamically Reshaping, High Data-Reuse Accelerator for Compact DNNs

ESSENCE: Exploiting Structured Stochastic Gradient Pruning for Endurance-Aware ReRAM-Based In-Memory Training Systems

ReaLPrune: ReRAM Crossbar-Aware Lottery Ticket Pruning for CNNs

ISLPED 2022: An Experience of a Hybrid Conference in the Time of COVID-19

MWSCAS Guest Editorial Special Issue Based on the 64th International Midwest Symposium on Circuits and Systems

Monolithic 3D stacking for neural network acceleration

Guest Editorial Special Issue on the International Symposium on Integrated Circuits and Systems—ISICAS 2022

Introduction to the Special Issue on Accelerating AI on the Edge – Part 2

Accelerating Large-Scale Graph Neural Network Training on Crossbar Diet

Collaborators (8)

Chun Jason Xue

Krishnendu Chakrabarty

Yiran Chen

Kaoutar El Maghraoui

Massimo Alioto

Xiaobo Hu

Miroslav Pajic

Keshab K. Parhi

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