December FPTalks Community Meeting: Debasmita Lohar on Aster

[Ian Briggs]

Howdy folks!

ThisThursday, December 5th at 9:00-10:00 AM pacific time, we’ll have the next FPTalks Community Meeting on this Zoom: https://washington.zoom.us/j/92831331326

We’re super excited to welcome Debasmita Lohar from Karlsruhe Institute of Technology to present on Aster: sound mixed fixed-point quantizer for neural networks.

Neural networks are increasingly becoming integral to safety-critical applications, such as controllers in embedded systems. While formal safety verification focuses on idealized real-valued networks, practical applications require quantization to finite precision, inevitably introducing roundoff errors. Manually optimizing precision, especially for fixed-point implementation, while ensuring safety is complex and time-consuming.

In this talk, I will introduce Aster, the sound, fully automated, mixed-precision, fixed-point quantizer for deep feed-forward neural networks. Aster reduces the quantization problem to a mixed-integer linear programming (MILP) problem, thus efficiently determining minimal precision to guarantee predefined error bounds. Our evaluations show that Aster's optimized code reduces machine cycles when compiled to an FPGA with a commercial HLS compiler compared to (sound) state-of-the-art. Furthermore, Aster handles significantly more benchmarks faster, especially for larger networks with thousands of parameters.

Looking forward to seeing everyone!

As a reminder, if you would like to give a talk or know of someone that would be great for an FPBench Community meeting, please have them fill out the speaker suggestion form!

- Ian