@inproceedings{ab493a90c7554e269401a7cec3aa45d8,
title = "A 19.4 nJ/decision 364K decisions/s in-memory random forest classifier in 6T SRAM array",
abstract = "This paper presents IC realization of a random forest (RF) machine learning classifier. Algorithm-architecture-circuit is co-optimized to minimize the energy-delay product (EDP). Deterministic subsampling (DSS) and balanced decision trees result in reduced interconnect complexity and avoid irregular memory accesses. Low-swing analog in-memory computations embedded in a standard 6T SRAM enable massively parallel processing thereby minimizing the memory fetches and reducing the EDP further. The 65nm CMOS prototype achieves a 6.8× lower EDP compared to a conventional design at the same accuracy (94%) for an 8-class traffic sign recognition problem.",
keywords = "In-memory computing, Machine learning, Pattern recognition, Random forest, Traffic sign recognition",
author = "Mingu Kang and Gonugondla, {Sujan K.} and Shanbhag, {Naresh R.}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 43rd IEEE European Solid State Circuits Conference, ESSCIRC 2017 ; Conference date: 11-09-2017 Through 14-09-2017",
year = "2017",
month = nov,
day = "2",
doi = "10.1109/ESSCIRC.2017.8094576",
language = "English (US)",
series = "ESSCIRC 2017 - 43rd IEEE European Solid State Circuits Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "263--266",
booktitle = "ESSCIRC 2017 - 43rd IEEE European Solid State Circuits Conference",
address = "United States",
}