We demonstrate an event-driven Deep Learning (DL) hardware software ecosystem. The user-friendly software tools port models from Keras (popular machine learning libraries), automaticaly convert DL models to Spiking equivalents, i.e. Spiking Convolutional Neural Networks (SCNNs) and run spiking simulations of the converted models on the hardware emulator for testing and prototyping. More importantly, the software ports the converted models onto a novel, ultra-low power, real-time, event-driven ASIC SCNN Chip: DynapCNN. An interactive demonstration of a real-time face recognition system built using the above pipeline is shown as an example.

Ferroelectric tunneling junctions (FTJ) are considered to be the intrinsically most energy efficient memristors. In this work, specific electrical features of ferroelectric hafnium-zirconium oxide based FTJ devices are investigated. Moreover, the impact on the design of FTJ-based circuits for edge computing applications is discussed by means of two example circuits.

Abstract-We use a large-scale analog neuromorphic system to encode the hidden-layer activations of a single-layer feed forward network with random weights. The random activations of the network are implemented using the device mismatch inherent to analog circuits. We show that these activations produced by analog VLSI implementations of integrate and fire neurons are suited to solve multi dimensional, non linear regression tasks. Exploitation of the device mismatch eliminates the storage requirements for the random network weights.

The sense of touch is essential in our everyday life as it allows us to interact with our environment. The same applies to robots and users of prostheses but requires sensing solutions that are power efficient and allow edge and embedded computation. In this paper, we present a capacitive, neuromorphic, eventdriven, tactile sensor. The mixed-mode subthreshold circuit is implemented in 180 nm technology and achieves a sensitivity of ≈ 30 Hz/N in simulation with the SPICE simulation platform spectre.