[CS] Mingyuan Xiang MS PresentationJun 4, 2025

[via cs]

This is an announcement of Mingyuan Xiang's MS Presentation
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Candidate: Mingyuan Xiang

Date: Wednesday, June 04, 2025

Time:  3 pm CST

Location: JCL 298

Title: Lupe: Integrating the Top-down Approach with DNN Execution on Ultra-Low-Power Devices

Abstract: Executing deep neural networks (DNNs) on ultra-low-power (ULP) microcontrollers creates enormous opportunities for new intelligent edge applications. However, manually writing optimized DNN programs for ULP devices is time-consuming and error prone due to the difficulty of managing on-device accelerators. Many prior works address this problem by creating special libraries that tailor common DNN building blocks for unique accelerators of ULP devices. This is a bottom-up approach, as developers build DNNs by assembling library calls. Unfortunately, the encapsulation overhead inherent in this approach greatly reduces accelerator utilization and overall performance. Instead, we advocate for a top-down approach.
We present Lupe, a code generation framework that converts high-level DNN algorithm descriptions to ULP-optimized code. Lupe provides top-down intermittent support that significantly reduces overhead while maintaining intermittent safety. We demonstrate Lupe's benefits on an MSP430, achieving 12.36× and 2.22× average speedup over two prior works across a variety of DNN models in continuous power. Moreover, Lupe reduces the average intermittent runtime costs of prior works by 96.65% and 71.15%, respectively.

Advisors: Hank Hoffmann

Committee: Hank Hoffmann, Pedro Lopes, and Kexin Pei