@inproceedings{eda2d58855574a9ea9fa46256b8649d6,
title = "GDOT: A graphene-based nanofunction for dot-product computation",
abstract = "Though much excitement surrounds two-dimensional (2D) beyond CMOS fabrics like graphene and MoS2, most efforts have focused on individual devices, with few high-level implementations. Here we present the first graphene-based dot-product nanofunction (GDOT) using a mixed-signal architecture. Dot product kernels are essential for emerging image processing and neuromorphic computing applications, where energy efficiency is prioritized. SPICE simulations of GDOT implementing a Gaussian blur show up to ∼104 greater signal-To-noise ratio (SNR) over CMOS based implementations-a direct result of higher graphene mobility in a circuit tolerant to low on/off ratios. Energy consumption is nearly equivalent, implying the GDOT can operate faster at higher SNR than CMOS counter-parts while preserving energy benefits over digital implementations. We implement a prototype 2-input GDOT on a wafer-scale 4″ process, with measured results confirming dot-product operation and lower than expected computation error.",
author = "Wang, {Ning C.} and Gonugondla, {Sujan K.} and Ihab Nahlus and Shanbhag, {Naresh R.} and Eric Pop",
year = "2016",
month = sep,
day = "21",
doi = "10.1109/VLSIT.2016.7573377",
language = "English (US)",
series = "Digest of Technical Papers - Symposium on VLSI Technology",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2016 IEEE Symposium on VLSI Technology, VLSI Technology 2016",
address = "United States",
note = "36th IEEE Symposium on VLSI Technology, VLSI Technology 2016 ; Conference date: 13-06-2016 Through 16-06-2016",
}