A reconfigurable analog substrate for highly efficient maximum flow computation

Abstract

We present the design and analysis of a novel analog reconfigurable substrate that enables fast and efficient computation of maximum flow on directed graphs. The substrate is composed of memristors and standard analog circuit components, where the on/off states of the crossbar switches encode the graph topology. We show that upon convergence, the steady-state voltages in the circuit capture the solution to the maximum flow problem. We also provide techniques to minimize the impacts of variability and non-ideal circuit components on the solution quality, enabling practical implementation of the proposed substrate. Performance evaluation demonstrates orders of magnitude improvements in speed and energy efficiency compared to a standard CPU implementation.

Exploiting deep learning accelerators for neuromorphic workloads

“…The neuromodulatory processes in the brain that leverage spikes to promote learning remain somewhat shrouded in mystery, which has inspired the development of several research-based neuromorphic processors. Several examples include Loihi developed by Intel Labs [32,33], IBM's TrueNorth [34,35], Neurogrid from Stanford University [36], SpiNNaker initiated at the University of Manchester [37,38], National University of Singapore's Shenjing [39], and memristor based accelerators like RENO [40], Harmonica [41], MNSIM [42], some of which have roused neuromorphic research ecosystems where hardware access is offered both remotely and physically to the broader research community. While such neuromorphic processors remain to be optimized for gradient-based learning, they have incited much interest in how neurobiological processes can be modelled in-silico.…”