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!
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.