Tiny Machine Learning for Ultra Low-Power Edge Computing

4 credits

Studies the design and deployment of machine learning (ML) models on ultra low-power edge devices such as microcontrollers and embedded sensors. Covers development and build of responsive, private, and reliable ML applications at the edge with compact and accurate models under tight memory, compute, and energy budgets. Course overlaps with: TCES 465. Prerequisite: E E 242; either AMATH 352, MATH 136, or MATH 208; and either IND E 315, MATH 394/STAT 394, STAT 390, or E E 391; recommended: E E 344; and familiarity with Python programming and basic C/C++programming. Offered: Sp.