EE Student Information

The Department of Electrical Engineering supports Black Lives Matter. Read more.

• • • • •

EE Student Information, Spring Quarter through Academic Year 2020-2021: FAQs and Updated EE Course List.

Updates will be posted on this page, as well as emailed to the EE student mail list.

Please see Stanford University Health Alerts for course and travel updates.

As always, use your best judgement and consider your own and others' well-being at all times.

Best paper, best poster award to Ning Wang at TECHCON

March 2017

Ning Wang, EE PhD candidate, received best paper and best poster awards at TECHCON 2016. The title of his paper is "GDOT: A Graphene-Based Nanofunction for Dot-Product Computation".

TECHCON is a technical conference and networking event for Semiconductor Research Corporation (SRC) members and students.

Ning Wang's research is in Physical Technology & Science and his advisor is Eric Pop.

 

Congratulations to Ning on his well-deserved recognition!

 

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 ~10(4) 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 counterparts while preserving energy benefits over digital implementations. We implement a prototype 2-input GDOT on a waferscale 4" process, with measured results confirming dot-product operation and lower than expected computation error.