The paper focuses on real-time facial expression recognition (FER) systems as an important component in various real-world applications such as social robotics. We investigate two hardware options for the deployment of FER machine learning (ML) models at the edge: neuromorphic hardware versus edge AI accelerators. Our study includes exhaustive experiments providing comparative analyses between the Intel Loihi neuromorphic processor and four distinct edge platforms: Raspberry Pi-4, Intel Neural Compute Stick (NSC), Jetson Nano, and Coral TPU. The results obtained show that Loihi can achieve approximately two orders of magnitude reduction in power dissipation and one order of magnitude energy savings compared to Coral TPU which happens to be the least power-intensive and energy-consuming edge AI accelerator. These reductions in power and energy are achieved while the neuromorphic solution maintains a comparable level of accuracy with the edge accelerators, all within the real-time latency requirements.

Neuromorphic chips have been endorsed in research showing that they are much more energy efficient at operating large deep learning networks than non-neuromorphic hardware. This may become important as AI adoption increases. The study was carried out by the Institute of Theoretical Computer Science at the Graz University of Technology (TU Graz) in Austria using Intel's Loihi 2 silicon, a second-generation experimental neuromorphic chip announced by Intel Labs last year that has about a million artificial neurons. Their research paper, "A Long Short-Term Memory for AI Applications in Spike-based Neuromorphic Hardware," published in Nature Machine Intelligence, claims that the Intel chips are up to 16 times more energy efficient in deep learning tasks than performing the same task on non-neuromorphic hardware. The hardware tested consisted of 32 Loihi chips.

Neuromorphic chips that mimic the way brains work may have broad applicability for high-performance computing applications and could be a better fit than CPUs and GPUs in some cases, according to Sandia National Laboratories in the US. Neuromorphic computing represents a fundamental change in the way data is processed and analyzed. Up until now, artificial intelligence has been promoted as the main use case, although IBM, which was far ahead of the commercially viable brain-inspired game with its True North devices once saw much broader applicability. Intel, for instance, positions its Loihi neurochips as the future of AI computing but as Sandia researchers demonstrated in a recent article in the peer-reviewed journal Nature Electronics, Intel's Loihi chips "can solve more complex problems than those posed by artificial intelligence and may even earn a place in high-performance computing." This includes problems like tracking X-rays passing through bone and soft tissue, data flows within social networks, financial market movements and disease spread within a population, among other things.

The great promises of neuromorphic sensing and processing for robotics have led researchers and engineers to investigate novel models for robust and reliable control of autonomous robots (navigation, obstacle detection and avoidance, etc.), especially for quadrotors in challenging contexts such as drone racing and aggressive maneuvers. Using spiking neural networks, these models can be run on neuromorphic hardware to benefit from outstanding update rates and high energy efficiency. Yet, low-level controllers are often neglected and remain outside of the neuromorphic loop. Designing low-level neuromorphic controllers is crucial to remove the standard PID, and therefore benefit from all the advantages of closing the neuromorphic loop. In this paper, we propose a parsimonious and adjustable neuromorphic PID controller, endowed with a minimal number of 93 neurons sparsely connected to achieve autonomous, onboard altitude control of a quadrotor equipped with Intel's Loihi neuromorphic chip. We successfully demonstrate the robustness of our proposed network in a set of experiments where the quadrotor is requested to reach a target altitude from take-off. Our results confirm the suitability of such low-level neuromorphic controllers, ultimately with a very high update frequency.

Neuromorphic processors like Loihi offer a promising alternative to conventional computing modules for endowing constrained systems like micro air vehicles (MAVs) with robust, efficient and autonomous skills such as take-off and landing, obstacle avoidance, and pursuit. However, a major challenge for using such processors on robotic platforms is the reality gap between simulation and the real world. In this study, we present for the very first time a fully embedded application of the Loihi neuromorphic chip prototype in a flying robot. A spiking neural network (SNN) was evolved to compute the thrust command based on the divergence of the ventral optic flow field to perform autonomous landing. Evolution was performed in a Python-based simulator using the PySNN library. The resulting network architecture consists of only 35 neurons distributed among 3 layers. Quantitative analysis between simulation and Loihi reveals a root-mean-square error of the thrust setpoint as low as 0.005 g, along with a 99.8% matching of the spike sequences in the hidden layer, and 99.7% in the output layer. The proposed approach successfully bridges the reality gap, offering important insights for future neuromorphic applications in robotics. Supplementary material is available at https://mavlab.tudelft.nl/loihi/.

We take our sense of touch for granted. Simple tasks like opening a jar or tying our shoelaces would be a whole lot more complex if we couldn't feel the object with our hands. Robots typically struggle to replicate this sense, restricting their ability to manipulate objects. But researchers from the National University of Singapore (NUS) might have found a solution: pairing artificial skin with a neuromorphic "brain." The system was developed by a team led by Assistant Professors Benjamin Tee, an electronic skin expert, and Harold Soh, an AI specialist.

The new system, which runs artificial intelligence software on Intel's Loihi neuromorphic chip, is essentially an "electronic nose" that can learn the scent of a chemical from a single exposure. Researchers at Cornell University and Intel have developed artificial intelligence (AI) software that can learn the scent of a chemical with just one exposure, and then remember that scent forever. The software, which is designed to run most efficiently on an experimental chip from Intel known as Loihi, is so precise, it can even detect a scent that's masked by a number of other scents, according to researchers. Ultimately, the researchers hope to produce a market-ready solution that can detect hazardous substances in the air, sniff out dangerous drugs, discover hidden explosives, and assist with medical diagnoses. "Low-energy modules built around Loihi, running our algorithm, and hooked-up to diverse sensor arrays could be built into robots, medical analysis devices; for example, blood composition, hyperspectral processors, air quality sensors, food processing pipelines, you name it," says Thomas A. Cleland, a member of the research team and associate chair and professor of psychology at Cornell University. The system works by processing an input signal pattern for a scent drawn from an array of sensors, then recording that signal pattern in the AI software as a recognizable scent for future use.

Intel's fifth-generation Loihi chip uses neuromorphic computing to learn faster on less training data than traditional artificial intelligence techniques -- including how to smell like a human does and make accurate conclusions based on a tiny dataset of essentially just one sample. "That's really one of the main things we're trying to understand and map into silicon … the brain's ability to learn with single examples," Mike Davies, the director of Intel's Neuromorphic Computing Lab, told me recently on The AI Show podcast. "So with just showing one clean presentation of an odor, we can store that in this high dimensional representation in the chip, and then it allows it to then recognize a variety of noisy, corrupted, occluded odors like you would be faced with in the real world." Neuromorphic computing has been around since the 1980s and is an attempt to use technology to mimic biological systems. Intel believes it is "the next generation of AI" and has designed its Loihi chip with neural units that approximate some functions of a human brain.

Intel has scaled up its neuromorphic computing system by integrating 768 of its Loihi chips into a 5 rack-unit system called Pohoiki Springs. This cloud-based system will be made available to Intel's Neuromorphic Research Community (INRC) to enable research and development of larger and more complex neuromorphic algorithms. Pohoiki Springs contains the equivalent of 100 million neurons, about the same number as in the brain of a small mammal such as a mole rat or a hamster. Neuromorphic Chip Intel debuted its Loihi neuromorphic chip for research applications in 2017. It mimics the architecture of the brain, using electrical pulses known as spikes, whose timing modulates the strength of the connections between neurons.

This morning, Intel announced the general readiness of Pohoiki Springs, a powerful self-contained neuromorphic system that's about the size of five standard servers. The company says the system will be available to members of the Intel Neuromorphic Research Community via the cloud using Intel's Nx SDK and community-contributed software components, giving them a tool to scale up their neuromorphic research and explore ways to accelerate workloads that run slowly on today's conventional architectures. Intel claims Pohoiki Springs, which was announced in July 2019, is similar in neural capacity to the brain of a small mammal, with 768 Loihi chips and 100 million neurons spread across 24 Arria10 FPGA Nahuku expansion boards (containing 32 chips each) that operate at under 500 watts. This is ostensibly a step on the path to supporting larger and more sophisticated neuromorphic workloads. In fact, just this week, Intel demonstrated that the chips can be used to "teach" an AI model to distinguish among 10 different scents. "Pohoiki Springs enables our research partners to explore ways to accelerate workloads that run slowly today on conventional architectures, including high-performance computing systems," said Intel neuromorphic compute lab director Mike Davies in a statement.