As we move towards more ubiquitous, always-on sensing and computing, power becomes increasingly important. There's perhaps no better an example of where this is important than the voice-activated devices on our desks, in our pockets, and distributed around our homes. As we saw last year, keyword spotting in particular is currently a target for all kinds of neuromorphic technologies. The 2020 winner of the Misha Mahowald Prize for Neuromorphic Engineering is Prof. Shih-Chii Liu and her team, who have been working on low-latency, low-power sensors for detecting speech. The dynamic audio sensors that Shih-Chii Liu and her team at the Institute of Neuroinformatics (INI) have been developing could eventually address this market.

Current hardware approaches to biomimetic or neuromorphic artificial intelligence rely on elaborate transistor circuits to simulate biological functions. However, these can instead be more faithfully emulated by higher-order circuit elements that naturally express neuromorphic nonlinear dynamics1–4. Generating neuromorphic action potentials in a circuit element theoretically requires a minimum of third-order complexity (for example, three dynamical electrophysical processes)5, but there have been few examples of second-order neuromorphic elements, and no previous demonstration of any isolated third-order element6–8. Using both experiments and modelling, here we show how multiple electrophysical processes—including Mott transition dynamics—form a nanoscale third-order circuit element. We demonstrate simple transistorless networks of third-order elements that perform Boolean operations and find analogue solutions to a computationally hard graph-partitioning problem. This work paves a way towards very compact and densely functional neuromorphic computing primitives, and energy-efficient validation of neuroscientific models. Electrophysical processes are used to create third-order nanoscale circuit elements, and these are used to realize a transistorless network that can perform Boolean operations and find solutions to a computationally hard graph-partitioning problem.

In this paper, we review recent work published over the last 3 years under the umbrella of Neuromorphic engineering to analyze what are the common features among such systems. We see that there is no clear consensus but each system has one or more of the following features:(1) Analog computing (2) Non vonNeumann Architecture and low-precision digital processing (3) Spiking Neural Networks (SNN) with components closely related to biology. We compare recent machine learning accelerator chips to show that indeed analog processing and reduced bit precision architectures have best throughput, energy and area efficiencies. However, pure digital architectures can also achieve quite high efficiencies by just adopting a non von-Neumann architecture. Given the design automation tools for digital hardware design, it raises a question on the likelihood of adoption of analog processing in the near future for industrial designs. Next, we argue about the importance of defining standards and choosing proper benchmarks for the progress of neuromorphic system designs and propose some desired characteristics of such benchmarks. Finally, we show brain-machine interfaces as a potential task that fulfils all the criteria of such benchmarks.

Neuromorphic engineering, also known as neuromorphic computing,[1][2][3] is a concept developed by Carver Mead,[4] in the late 1980s, describing the use of very-large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures present in the nervous system.[5] In recent times, the term neuromorphic has been used to describe analog, digital, mixed-mode analog/digital VLSI, and software systems that implement models of neural systems (for perception, motor control, or multisensory integration). The implementation of neuromorphic computing on the hardware level can be realized by oxide-based memristors,[6] spintronic memories,[7] threshold switches, and transistors.[8] A key aspect of neuromorphic engineering is understanding how the morphology of individual neurons, circuits, applications, and overall architectures creates desirable computations, affects how information is represented, influences robustness to damage, incorporates learning and development, adapts to local change (plasticity), and facilitates evolutionary change. Neuromorphic engineering is an interdisciplinary subject that takes inspiration from biology, physics, mathematics, computer science, and electronic engineering to design artificial neural systems, such as vision systems, head-eye systems, auditory processors, and autonomous robots, whose physical architecture and design principles are based on those of biological nervous systems.[9]

There are a number of types and styles of artificial intelligence, but there's a key difference between the branch of programming that looks for interesting solutions to pertinent problems, and the branch of science seeking to model and simulate the functions of the human brain. Neuromorphic computing, which includes the production and use of neural networks, deals with proving the efficacy of any concept of how the brain performs its functions -- not just reaching decisions, but memorizing information and even deducing facts. Both literally and practically, "neuromorphic" means "taking the form of the brain." The key word here is "form," mainly because so much of AI research deals with simulating, or at least mimicking, the function of the brain. The engineering of a neuromorphic device involves the development of components whose functions are analogous to parts of the brain, or at least to what such parts are believed to do.

According to the Times, true artificial intelligence is just around the corner. A year ago, the paper ran a front-page story about the wonders of new technologies, including deep learning, a neurally-inspired A.I. technique for statistical analysis. Then, among others, came an article about how I.B.M.'s Watson had been repurposed into a chef, followed by an upbeat post about quantum computation. On Sunday, the paper ran a front-page story about "biologically inspired processors," "brainlike computers" that learn from experience. This past Sunday's story, by John Markoff, announced that "computers have entered the age when they are able to learn from their own mistakes, a development that is about to turn the digital world on its head."