ECE 498NSU/598NSG: Deep Learning in Hardware

Course Description

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  • Description: This course will present challenges in implementing deep learning algorithms on resource-constrained hardware platforms at the Edge such as wearables, IoTs, autonomous vehicles, and biomedical devices. Fixed-point requirements of deep for deep neural networks and convolutional neural networks including the back-prop based training will be studied. Algorithm-to-architecture mapping techniques will be explored to trade-off energy-latency-accuracy in deep learning digital accelerators and analog in-memory architectures. Fundamentals of learning behavior, fixed-point analysis, architectural energy and delay models will be introduced in just-in-time manner throughout the course. Case studies of hardware (architecture and circuit) realizations of deep learning systems will be presented. Homeworks will include a mix of analysis and programming exercises in Python and Verilog leading up to a term project.

  • Syllabus: ECE 498NSU/598NSG syllabus

  • Prerequisite: ECE 385 and ECE 313 or equivalent. Students should be familiar with programming in Python. HDL (Verilog) programming experience is desirable.

  • Time and Place: 11:00am-12:20pm, TuTh ONL


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  • Professor Naresh Shanbhag

  • Department of Electrical and Computer Engineering

  • Web page:

  • Office/Recitation Hours: 2:00pm-3:00pm Tu ONL

  • Email: shanbhag AT illinois DOT edu

Teaching Assistants

  • Rex Geng

    • Email: hgeng4 AT illinois DOT edu

    • Office/Recitation Hours: 5:00pm-6:00pm Th ONL

  • Howard Li

    • Email: hl11 AT illinois DOT edu

    • Office/Recitation Hours: 4:00pm-5:00pm W ONL


  • 8/24/2020: Welcome to ECE 498NSU/598NSG!