Fall 2016: CS 7960 Special Topics: Neuromorphic Architectures

General Information:

Course Description:

The course will cover hardware approaches for implementing neural-inspired algorithms. Neural-inspired algorithms use a variety of models for (i) the neuron (e.g., perceptrons and spiking neurons), (ii) connectivity among neurons (e.g., feed-forward, recurrent, reservoir), (iii) training (e.g., back-propagation and {brace yourself} spike timing dependent plasticity), etc. The course will briefly discuss these algorithms, but will primarily focus on state-of-the-art hardware approaches to implement these algorithms. These approaches will ultimately yield accelerator chips that will be used for a variety of cognitive tasks in datacenters, mobile devices, self-driving cars, etc.

The course does not have any formal pre-requisites, but is intended primarily for graduate students with some familiarity in architecture and/or machine learning. The lectures will be self-contained, i.e., I will provide sufficient background in architecture and machine learning to make the material accessible. Most class lectures will be based on recent research papers (see tentative schedule below). Students will also work in groups on semester-long projects -- the projects will compare the implementations of various cognitive tasks with different algorithms and hardware approaches.

College of Engineering Policies (Disability, Add, Drop, Appeals, etc.):

Guidelines from the college.


The following is a tentative guideline and may undergo changes. The class project accounts for 50% of the final grade. 40% will be based on two take-home exams. 10% will be based on class participation and class presentations.

Tentative Class Schedule

Dates Lecture Topic Slides
Week 1: Aug 23/25 Overview, history of neural-based hardware  
Week 2: Aug 30/Sep 1 Machine learning primer  
Week 3: Sep 6/8 Hardware approaches for perceptron-based algorithms -- digital accelerators, data compression  
Week 4: Sep 13/15 Hardware approaches for perceptron-based algorithms -- analog circuits, tools  
Week 5: Sep 20/22 Other hardware approaches for perceptrons  
Week 6: Sep 27/29 Spiking neural networks  
Week 7: Oct 4/6 Spiking neural networks  
Week 8: Oct 11/13 Fall Break  
Week 9: Oct 18/20 Architectures for spiking: TrueNorth  
Week 10: Oct 25/27 Architectures for spiking: Spinnaker, Neurogrid, etc.  
Week 11: Nov 1/3 Comparison of perceptrons vs. spiking  
Week 12: Nov 8/10 Emerging machine learning algorithms: LSTMs, reservoir computing, recurrent networks, etc.  
Week 13: Nov 15/17 Project presentations  
Week 14: Nov 22 TBD  
Week 15: Nov 29/ Dec 1 Precision medicine applications  
Week 16: Dec 6/8 Project presentations