While Moore’s law has driven exponential computing power expectations, its nearing end calls for new avenues for improving the overall system performance. One of these avenues is the exploration of alternative brain-inspired computing architectures that aim at achieving the flexibility and computational efficiency of biological neural processing systems. Within this context, neuromorphic engineering represents a paradigm shift in computing based on the implementation of spiking neural network architectures in which processing and memory are tightly colocated. In this article, we provide a comprehensive overview of the field, highlighting the different levels of granularity at which this paradigm shift is realized and comparing design approaches that focus on replicating natural intelligence (bottom-up) versus those that aim at solving practical artificial intelligence applications (top-down). First, we present the analog, mixed-signal, and digital circuit design styles, identifying the boundary between processing and memory through time multiplexing, in-memory computation, and novel devices. Then, we highlight the key tradeoffs for each of the bottom-up and top-down design approaches, survey their silicon implementations, and carry out detailed comparative analyses to extract design guidelines. Finally, we identify necessary synergies and missing elements required to achieve a competitive advantage for neuromorphic systems over conventional machine-learning accelerators in edge computing applications and outline the key ingredients for a framework toward neuromorphic intelligence.@en

Low-power event-based analog front-ends (AFE) are a crucial component required to build efficient end-to-end neuromorphic processing systems for edge computing. Although several neuromorphic chips have been developed for implementing spiking neural networks (SNNs) and solving a wide range of sensory processing tasks, there are only a few general-purpose analog front-end devices that can be used to convert analog sensory signals into spikes and interfaced to neuromorphic processors. In this work, we present a novel, highly configurable analog front-end chip, denoted as "SPAIC" (signal-to-spike converter for analog AI computation), that offers a general-purpose dual-mode analog signal-to-spike encoding with delta modulation and pulse frequency modulation, with tunable frequency bands. The ASIC is designed in a 180nm process. It supports and encodes a wide variety of signals spanning 4 orders of magnitude in frequency, and provides an event-based output that is compatible with existing neuromorphic processors. We validated the ASIC for its functions and present initial silicon measurement results characterizing the basic building blocks of the chip.@en

Due to its intrinsic sparsity both in time and space, event-based data is optimally suited for edge-computing applications that require low power and low latency. Time varying signals encoded with this data representation are best processed with Spiking Neural Networks (SNN). In particular, recurrent SNNs (RSNNs) can solve temporal tasks using a relatively low number of parameters, and therefore support their hardware implementation in resource-constrained computing architectures. These premises propel the need of exploring the properties of these kinds of structures on low-power processing systems to test their limits both in terms of computational accuracy and resource consumption, without having to resort to full-custom implementations. In this work, we implemented an RSNN model on a low-end, resource-constrained ARM-Cortex-M4-based Micro Controller Unit (MCU). We trained it on a down-sampled version of the N-MNIST event-based dataset for digit recognition as an example to assess its performance in the inference phase. With an accuracy of 97.2%, the implementation has an average energy consumption as low as 4.1μJ and a worst-case computational time of 150.4μs per time-step with an operating frequency of 180 MHz, so the deployment of RSNNs on MCU devices is a feasible option for small image vision real-time tasks.

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