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GDOT: A graphene-based nanofunction for dot-product computation
- Source :
- 2016 IEEE Symposium on VLSI Technology.
- Publication Year :
- 2016
- Publisher :
- IEEE, 2016.
-
Abstract
- Though much excitement surrounds two-dimensional (2D) beyond CMOS fabrics like graphene and MoS 2 , 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.
- Subjects :
- 010302 applied physics
business.industry
Computer science
Spice
Electrical engineering
Gaussian blur
Dot product
02 engineering and technology
Energy consumption
021001 nanoscience & nanotechnology
01 natural sciences
symbols.namesake
Beyond CMOS
CMOS
Neuromorphic engineering
0103 physical sciences
Hardware_INTEGRATEDCIRCUITS
symbols
Electronic engineering
0210 nano-technology
business
Efficient energy use
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2016 IEEE Symposium on VLSI Technology
- Accession number :
- edsair.doi...........bee124625934cb97b389cc26858e737b