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Subject offerings of new and developing areas of knowledge in electrical and computer engineering intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites. Course Information: 0 to 4 undergraduate hours. 0 to 4 graduate hours. May be repeated in the same or separate terms if topics vary.

Resource - Constrained Machine Learning for Edge Applications. 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 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. Prerequisites: ECE 313 and ECE 385