Workshop on Hardware and Algorithms for Learning On-a-chip
(HALO) 2020

Thursday, November 5, 2020

Virtual Workshop Event

Past HALO workshops : 2015/2016/2017/2018/2019

General Information

In recent years, machine/deep learning algorithms has unprecedentedly improved the accuracies in practical recognition and classification tasks, some even surpassing human-level accuracy. While significant progresses have been made on accelerating the models for real-time inference on edge and mobile devices, the training of the models largely remains offline on server side. State-of-the-art learning algorithms for deep neural networks (DNN) imposes significant challenges for hardware implementations in terms of computation, memory, and communication. This is especially true for edge devices and portable hardware applications, such as smartphones, machine translation devices, and smart wearable devices, where severe constraints exist in performance, power, and area.

There is a timely need to map the latest complex learning algorithms to custom hardware, in order to achieve orders of magnitude improvement in performance, energy efficiency and compactness. Exemplary efforts from industry and academia include many application-specific hardware designs (e.g., xPU, FPGA, ASIC, etc.). Recent progress in computational neurosciences and nanoelectronic technology, such as emerging memory devices, will further help shed light on future hardware-software platforms for learning on-a-chip. At the same time new learning algorithms need to be developed to fully explore the potential of the hardware architecture.

The overarching goal of this workshop is to explore the potential of on-chip machine learning, to reveal emerging algorithms and design needs, and to promote novel applications for learning. It aims to establish a forum to discuss the current practices, as well as future research needs in the aforementioned fields.

Key Topics

  • Synaptic plasticity and neuron motifs of learning dynamics
  • Computation models of cortical activities
  • Sparse learning, feature extraction and personalization
  • Deep learning with high speed and high power efficiency
  • Hardware acceleration for machine learning
  • Hardware emulation of brain
  • Nanoelectronic devices and architectures for neuro-computing
  • Applications of learning on a smart mobile platform



  • Mike Davies, Intel

Invited Speakers

  • Nathan McDonald, Air Force Research Lab
  • Priya Panda, Yale University
  • Travis Dewolf, Applied Brain Research
  • Hai (Helen) Li, Duke University
  • Deming Chen, University of Illinois Urbana-Champaign
  • Yiyu Shi, University of Notre Dame
  • Yanzhi Wang, Northeastern University
  • Eriko Nurvitadhi, Intel

Preliminary Program

8:20am — 8:30am

Introduction and opening remarks

8:30am — 9:15am

——— Keynote talk ———

Mike Davies (Intel)

——— Session 1: Architecture and Algorithm for On-Chip-Learning ———

Section Chair: Qinru Qiu

9:15am — 9:40am

Nathan McDonald (Air Force Research Lab)

9:40am — 10:05am

Priya Panda (Yale University)

10:05am — 10:30am

Travis Dewolf (Applied Brain Research)

10:30am — 10:55am

Hai (Helen) Li (Duke University)

10:55am — 11:10am


——— Session 2: Intelligent Mobile Applications: learning and inference ———

Section Chair: Yingyan Lin

11:10am — 11:35am

Deming Chen (University of Illinois Urbana-Champaign)

11:35am — 12:00pm

Yiyu Shi (University of Notre Dame)

12:00pm — 12:25pm

Yanzhi Wang (Northeastern University)

12:25pm — 12:50pm

Eriko Nurvitadhi (Intel)

12:50pm — 1:05pm


Organizing Committee


  • Qinru Qiu (Syracuse University)
  • Yingyan Lin (Rice University)
  • Chenchen Liu (University of Maryland, Baltimore County)

Steering Committee

  • Yu Cao, Arizona State University
  • Xin Li, Duke University
  • Jae-sun Seo, Arizona State University

Technical Program Committee

  • Rob Aitken, ARM
  • Shawn Blanton, Carnegie Mellon University
  • Sankar Basu, National Science Foundation
  • Yiran Chen, Duke University
  • Kailash Gopalakrishnan, IBM
  • Yiorgos Makris, University of Texas, Dallas
  • Kendel McCarley, Raytheon Company
  • Mustafa Ozdal, Bilkent University, Turkey
  • Yanzhi Wang, Northeastern University
  • Yuan Xie, University of California, Santa Barbara
  • Jishen Zhao, University of California at San Diego
  • Qinru Qiu, Syracuse University
  • Yingyan Lin, Rice University

Sponsored by


Last updated on Oct. 18, 2020. Contents subject to change. © All rights reserved.