Neuromorphic engineering needs closed-loop benchmarks

Neuromorphic engineering aims to build (autonomous) systems by mimicking biological systems. It is motivated by the observation that biological organisms—from algae to primates—excel in sensing their environment, reacting promptly to their perils and opportunities. Furthermore, they do so more resiliently than our most advanced machines, at a fraction of the power consumption. It follows that the performance of neuromorphic systems should be evaluated in terms of real-time operation, power consumption, and resiliency to real-world perturbations and noise using task-relevant evaluation metrics. Yet, following in the footsteps of conventional machine learning, most neuromorphic benchmarks rely on recorded datasets that foster sensing accuracy as the primary measure for performance. Sensing accuracy is but an arbitrary proxy for the actual system's goal—taking a good decision in a timely manner. Moreover, static datasets hinder our ability to study and compare closed-loop sensing and control strategies that are central to survival for biological organisms. This article makes the case for a renewed focus on closed-loop benchmarks involving real-world tasks. Such benchmarks will be crucial in developing and progressing neuromorphic Intelligence. The shift towards dynamic real-world benchmarking tasks should usher in richer, more resilient, and robust artificially intelligent systems in the future

Open source 3D scanning and printing for design capture and realization

Traditionally, the complexities and costs associated with design conceptualization (e.g., 3D scanning) and design realization (e.g., 3D printing) have limited the diversity of individuals capable of participating in the process to individuals/entities with advanced technical backgrounds or substantial financial resources. The authors of this work propose a methodology that utilizes low cost hardware and open source software to make the capture, reuse and management of design knowledge more accessible to the general public. 3D scanners are digital tools that facilitate the conversion of physical object information into the digital space through multiple image capture techniques. 3D scanners have the potential to revolutionize design conceptualization in society by enabling individuals to seamlessly transform physical representations of objects into a digital 3D rendered version. The 3D rendered version can then be manipulated using existing 3D CAD tools (e.g., SolidWorks) and subsequently printed using a 3D printer. Design realization via 3D printers (e.g., RepRaps) is becoming an integral aspect of the engineering design process. While the conceptualization of designs (e.g. CAD models) helps designers visually experience potential candidate designs, product prototypes that can actually be touched and manipulated add an important ‘feedback’ dimension to the engineering design process. This scan-edit-print approach to design conceptualization and realization will enable designers collaborating in online environments to work towards achieving a common design by providing them with tools and techniques. A case study is presented that demonstrates the feasibility of the scan (knowledge capture), edit (knowledge reuse) and print (knowledge management) approach to design using low cost hardware and open source software

Event camera simulator improvements via characterized parameters

It has been more than two decades since the first neuromorphic Dynamic Vision Sensor (DVS) sensor was invented, and many subsequent prototypes have been built with a wide spectrum of applications in mind. Competing against state-of-the-art neural networks in terms of accuracy is difficult, although there are clear opportunities to outperform conventional approaches in terms of power consumption and processing speed. As neuromorphic sensors generate sparse data at the focal plane itself, they are inherently energy-efficient, data-driven, and fast. In this work, we present an extended DVS pixel simulator for neuromorphic benchmarks which simplifies the latency and the noise models. In addition, to more closely model the behaviour of a real pixel, the readout circuitry is modelled, as this can strongly affect the time precision of events in complex scenes. Using a dynamic variant of the MNIST dataset as a benchmarking task, we use this simulator to explore how the latency of the sensor allows it to outperform conventional sensors in terms of sensing speed