Mon 24 Jun 2019 09:25 - 09:45 at 224AB - Language Design I Chair(s): Benjamin Zorn

Recent advances in machine learning (ML) have produced KiloByte-size models that can directly run on constrained IoT devices. This approach avoids expensive communication between IoT devices and the cloud, thereby enabling energy-efficient real-time analytics. However, ML models are expressed typically in floating-point, and IoT hardware typically does not support floating-point. Therefore, running these models on IoT devices requires simulating IEEE-754 floating-point using software, which is very inefficient.

We present SeeDot, a domain-specific language to express ML inference algorithms and a compiler that compiles SeeDot programs to fixed-point code that can efficiently run on constrained IoT devices. We propose 1)~a novel compilation strategy that reduces the search space for some key parameters used in the fixed-point code, and 2)~new efficient implementations of expensive operations. SeeDot compiles state-of-the-art KB-sized models to various microcontrollers and low-end FPGAs. We show that SeeDot outperforms 1) software emulation of floating-point (Arduino), 2) high-bitwidth fixed-point (MATLAB), 3) post-training quantization (TensorFlow-Lite), and 4) floating- and fixed-point FPGA implementations generated using high-level synthesis tools.

Mon 24 Jun

Displayed time zone: Tijuana, Baja California change

08:45 - 09:45
Language Design IPLDI Research Papers at 224AB
Chair(s): Benjamin Zorn Microsoft Research
08:45
20m
Talk
LoCal: A Language for Programs Operating on Serialized Data
PLDI Research Papers
Michael Vollmer Indiana University, USA, Chaitanya S. Koparkar Indiana University, Mike Rainey Indiana University, USA, Laith Sakka Purdue University, Milind Kulkarni Purdue University, Ryan R. Newton Indiana University
DOI Authorizer link Pre-print Media Attached
09:05
20m
Talk
Scenic: A Language for Scenario Specification and Scene Generation
PLDI Research Papers
Daniel J. Fremont University of California at Berkeley, USA, Tommaso Dreossi University of California at Berkeley, Shromona Ghosh University of California at Berkeley, USA, Xiangyu Yue University of California at Berkeley, USA, Alberto L. Sangiovanni-Vincentelli University of California at Berkeley, USA, Sanjit Seshia UC Berkeley
Media Attached
09:25
20m
Talk
Compiling KB-Sized Machine Learning Models to Tiny IoT Devices
PLDI Research Papers
Sridhar Gopinath Microsoft Research, India, Nikhil Ghanathe Microsoft Research, India, Vivek Seshadri Microsoft Research, India, Rahul Sharma Microsoft Research
Link to publication DOI Media Attached