Booz Allen Hamilton Colloquium: Laura Fick, Mythic AI

Friday, September 16, 2022
3:30 p.m.-4:30 p.m.
Jeong H. Kim Engineering Building, Room 1110
Darcy Long
301 405 3114
dlong123@umd.edu

Speaker:  Laura Fick, Head of Mixed Signal Circuits Reserach at Mythic

Title: "Compute-in-Memory Processing for Energy Efficient Algorithms"

Abstract: AI and many other applications have opportunities to build systems that merge memory and computing into a unified structure in ways which yield significant improvements in energy efficiency, performance, and cost. In these scenarios, "moving the compute to the memory" makes sense because the applications have large amounts of data to process and relatively simple operations to perform. Traditionally, moving the data to the main system processor would be slow and inefficient, whereas creating specialized processing near the memory is more efficient. These approaches have a have a wide variety of applications as well as approaches. On one end of the spectrum, systems which have processing inside of an SSD to perform searches inside the drive itself, and on the other end of the spectrum, systems that have analog compute performing mathematics directly on the bitlines of the memory arrays. In this talk we will provide an overview of many of these approaches as well as tradeoffs in approaches for energy efficiency.

Bio: Laura Fick is a founding engineer at Mythic, an Austin TX based startup that is creating the next generation of AI inference microchips. Mythic uses mixed-signal computing to achieve 20-100x improvements in neural network energy efficiency and performance and has raised $70 million from top tier investors to execute on this vision. She received her bachelor’s degree in electrical engineering from the University of Maryland College Park, and master’s and PhD from the University of Michigan Ann Arbor. Her PhD thesis on analog compute in flash memory was the basis for Mythic’s technology. Since then, she has developed and patented new analog compute circuitry, bridged software and hardware development as the Director of AI Silicon Engineering, and currently is working as Analog Compute System Architect, designing next-generation products.

 

Audience: Clark School  All Students  Graduate  Undergraduate  Prospective Students  Faculty  Staff 

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