ELEG/CISC x87-022: VIP:
Neuromorphic Computing
Fall 2020, University
of Delaware
TL;DR: This vertically integrated project (VIP) course
focuses on building artificial intelligence devices with brain-like
energy-efficiency.
For an overview of the VIP program at UD see refer to the site: https://vip.udel.edu/
VIP
Advisor : Dr. Vishal Saxena (Website)
Email :
vsaxena@udel.edu
Class Schedule : We 5:40PM6:30PM
Class
dates : Sep 1 Dec 10, 2020
Office Hours :
Tue & Thu 2-3 PM
Slack
Channel : TBD
Textbook None. Datasheets, papers
and documents will be provided as needed.
Course
content (Syllabus)
https://vip.udel.edu/resource/VIP%20Syllabus
Goals: Develop new generation of
computing hardware and algorithms that can realized Artificial Intelligence
(AI) on portable edge devices. Emphasis will be on using neural-inspired, or
Neuromorphic Architectures, that can allow brain-like energy efficiency. The
devices would be able to process vision, speech, text, and sensor signals, and
make decisions based on learned behavior.
Key
Elements: Neuromorphic Computing, Artificial Intelligence at the Edge; Deep
learning, Artificial Neural Networks, Development of CMOS circuits using
emerging devices, In-Memory Computing, Implementation of learning algorithms on
available Edge-AI/neuromorphic hardware platforms and FPGAs.
Research Issues:
Development of neuromorphic computers involves challenges at the electronic
devices, circuits, and algorithm level. Not only each of these components have
their own research challenges, and integrative approach is followed where they
are synergistically developed. This research encompasses a range of projects
including:
1. Spiking
neural network (SNN) algorithm development on CPU/GPU, available neuromorphic
hardware platforms, and FPGAs
2. Development
of novel circuits using emerging memory devices such as RRAM, PCRAM and
spintronics
3. Development
of custom Neuromorphic Systems-on-a-Chip (SoCs) using CMOS technology
integrated with memory devices
4. Sensor
development and interfacing with neuromorphic platforms.
Major, Preparation and Interests:
Computer Engineering Integrated Circuits, Computer Architecture, Embedded
Systems; Electrical Engineering Machine Learning, Signal Processing; Computer
Science Neural Networks, Algorithms, Programming/Software; Biomedical
Engineering Sensor readout and signal processing.
Research Areas:
Computer Engineering, Integrated Circuits and Systems, Machine Learning,
Computational Neuroscience
Prerequisites Instructor permission.
Workload (Grading)
100% Project
report