We design and fabricate a flow-based circuit for edge detection in images that exploits device-level parallelism in nanoscale memristor crossbars. In our approach, a corpus of human-labeled edges in BSDS500 images is used to learn an edge detection function with ternary values: True, false, and don't-care. A Boolean crossbar design implementing an approximation of this ternary function using in-memory flow-based computing is then obtained using a massively parallel simulated annealing search executed on GPUs. We demonstrate the success of our approach by fabricating the memristor circuit on a 300mm wafer platform using a custom 65nm CMOS/ReRAM process technology. We demonstrate that our flow-based computing approach is either faster, more energy-efficient or produces fewer incorrect edges than other competing approaches. We show that our design has power and area requirements that are 3.3x and 2.5x lower, respectively, than the previous state-of-The-Art.
|Original language||English (US)|
|Number of pages||5|
|Journal||IEEE Transactions on Circuits and Systems II: Express Briefs|
|State||Published - May 2020|
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering