Design and Optimization of Temporal Encoders using Integrate-and-Fire and Leaky Integrate-and-Fire Neurons

As Moore's law nears its limit, a new form of signal processing is needed. Neuromorphic computing has used inspiration from biology to produce a new form of signal processing by mimicking biological neural networks using electrical components. Neuromorphic computing requires less signal preprocessing than digital systems since it can encode signals directly using analog temporal encoders from Spiking Neural Networks (SNNs). These encoders receive an analog signal as an input and generate a spike or spike trains as their output. The proposed temporal encoders use latency and Inter-Spike Interval (ISI) encoding and are expected to produce a highly sensitive hardware implementation of time encoding to preprocess signals for dynamic neural processors. Two ISI and two latency encoders were designed using Integrate-and-Fire (IF) and Leaky Integrate-and-Fire (LIF) neurons and optimized to produce low area designs. The IF and LIF neurons were designed using the Global Foundries 180nm CMOS process and achieved an area of 186µm2 and 182µm2, respectively. All four encoders have a sampling frequency of 50kHz. The latency encoders achieved an average energy consumption per spike of 277nJ and 316pJ for the IF-based and LIF-based latency encoders, respectively. The ISI encoders achieved an average energy consumption per spike of 1.07uJ and 901nJ for the IF-based and LIF-based ISI encoders, respectively. Power consumption is proportional to the number of neurons employed in the encoder and the potential to reduce power consumption through layout-level simulations is presented. The LIF neuron is able to use a smaller membrane capacitance to achieve similar operability as the IF neuron and consumes less area despite having more components. This demonstrates that capacitor sizes are the main limitations of a small size in spiking neurons for SNNs. An overview of the design and layout process of the two presented neurons is discussed with tips for overcoming problems encountered. The proposed designs can result in a fast neuromorphic process by employing a frequency higher than 10kHz and by providing a hardware implementation that is efficient in multiple sectors like machine learning, medical implementations, or security systems since hardware is safer from hacks.