Sangya Dutta1 Tanmay Chavan1 Shashwat Shukla1 Aditya Shukla1 Vinay Kunar1 Nihar Mohapatra2 Udayan Ganguly1

1, IIT Bombay, Mumbai, , India
2, IIT Gandhinagar, Gandhinagar, Gujarat, India

Spiking Neural Networks propose to mimic nature’s way of recognizing patterns and making decisions in a fuzzy manner. To develop such networks in hardware – two requirements are key. First, extremely large networks are needed in order to emulate the brain of advanced organisms e.g. human brain consists of 100 billion neurons each connected to 10000 synapses. This level of integration is challenging even for silicon-based VLSI Technology. Second, another challenge is to mimic the extreme energy efficiency of the biological brain. This requires highly scaled and energy efficient components like neurons with the ability to enable Very Large Scale Integration (VLSI). We have proposed a silicon-based leaky integrate and fire (LIF) neuron that is based on conventional silicon-on-insulator (SOI) technology [1]. The floating body effect of the partially depleted SOI transistor is used to store “holes” generated by impact ionization in the floating body, which performs the “integrate” function. Recombination or equivalent hole loss mimics the “leak” functions. The “hole” storage reduces the source barrier to increase the transistor current. Upon reaching a threshold current level, an external circuit records a “firing” event and resets the SOI MOSFET by draining all the stored holes. While the Leaky Integrate and Fire operation are mimicked well, which is equivalent to regular or fast spiking, there are other spiking patterns that biological neurons are able to drum out different spiking patterns e.g. intrinsically bursting or chattering dynamics. This three-terminal device is able to mimic these dynamics of biological spiking patterns. We will explore the design space with TCAD simulations. In terms of application, the neuron is able to show classification problems with reasonable accuracy, as well as some navigation problems. The neuron is compared with the other approaches in terms of areal and energy efficiency.

1. S. Dutta, V. Kumar, A. Shukla, N. R. Mohapatra, and U. Ganguly, “Leaky Integrate and Fire Neuron by Charge-Discharge Dynamics in Floating-Body MOSFET” Scientific Reports, Article No. 8257, 2017