Israeli fabless semiconductor company Polyn has announced the availability of neuromorphic analog signal processing (NASP) models for Edge Impulse, a development platform for machine learning on edge devices. Edge impulse provides a way for developers to compare models and their performance, and Polyn is making its models available on the platform to enable such evaluations, CEO and founder Aleksandr Timofeev said in an interview with EE Times Europe. "Polyn is comfortable with this comparison, as it is confident in its promise of offering chips that consume 100 microwatts of power, and no other competitor offers the same," said Timofeev, adding that the company pays a licensing fee to make models available on Edge Impulse. Current ML implementation methods rely on digitizing the generated data and then running them through digital ML frameworks, a process that involves considerable computational power. Processing raw sensor data in analog form can lead to decreased power consumption and increased accuracy for all applications compared with traditional, digital algorithm-based computing, Timofeev said.

Biomimicry, the science-slash-art of copying natural structures, is not a new idea. For decades, we have been trying to copy biological brains to make efficient computers, only slightly deterred by the fact that we don't know how biological intelligence works exactly. Armed with our best guesses, we developed models of the neuron and spiking neural networks based on the human brain, and we are now trying to develop these in silicon. Silicon imitations generally use simplified versions of the neuron, but they can still offer distinct advantages to edge applications that need fast, energy-efficient processing to make decisions. ABI Research reports that 4.6 billion sensors will ship in 2027, embedded in smart-home devices, robots, and appliances, up from 1.8 billion in 2021.

Cybersecurity is becoming a fundamental concern for the development of autonomous vehicle (AV) systems, as attacks can have serious consequences for autonomous electric vehicles and can put human lives at risk. Software attacks include data-driven decisions negatively impacting the autonomy of EVs and compromising the benefits of autonomous cars. AVs have seen many recent advances, with the integration of technologies like edge computing, private 5G, and high-performance processing units. In autonomous EVs, edge computing helps process the high volume of data at the edge to reduce latency and help vehicles make data-driven decisions in real time. Edge sensors deployed in vehicles have the scarcity of resources but require high computational power to process data.

BrainChip, a neuromorphic computing IP vendor, and Edge Impulse, an embedded machine-learning (ML) development platform vendor, have partnered to address the growing demand for large-scale edge AI deployment. The collaboration aims to strengthen the training AI workloads and inference deployment of computer vision and natural-language processing models on the edge network. Customers will now be able to develop integrated hardware and software solutions, which will help accelerate the adoption of ML at the edge. The collaboration aims to deliver platforms to customers looking to develop products that utilize the companies' ML capabilities, partners said in a statement. This announcement will help enterprise edge-computing deployment at scale gain traction in a wide range of industries, including health care, automotive, and military and aerospace.

Qu&Co and Pasqal are merging their businesses, combining Qu&Co's robust portfolio of algorithms with Pasqal's full-stack neutral-atom system to accelerate the quantum path to commercial applications. The united business, known as Pasqal and located in Paris, will offer a 1,000-qubit quantum solution in 2023, according to the disclosed roadmaps of the most sophisticated quantum platforms. Qu&Co's portfolio of quantum algorithms will be tightly integrated with Pasqal's advanced quantum hardware, providing added value to customers such as Johnson & Johnson, LG, Airbus, and BMW Group. The combined company will offer a wide range of quantum solutions in chemistry, life sciences, automotive, electronics, utilities, aerospace, defense, finance, and other sectors. In an interview with EE Times Europe, Georges-Olivier Reymond, CEO of Pasqal, said the merger enables the combined company to fast-track the implementation of its R&D roadmap, recruit top talent, achieve an industry-relevant quantum advantage much sooner, and serve more clients with new, unique, and proprietary quantum solutions.

Artificial intelligence is a misnomer. AI is neither artificial nor intelligent. The implication is that AI is analogous to human intelligence, but AI requires extensive human training to function, and it exhibits completely different logic from humans in terms of recognizing, understanding, and classifying objects or scenes. AI often lacks any semblance of common sense, can be easily fooled or corrupted, and can fail in unexpected and unpredictable ways. In other words, proceed with caution.

Over the last couple of days, I've had a lot of time (as well as many others) to think about COVID-19, and whether any AI technology can predict the pandemic virus outbreak patterns and warn us of its intended path. While many brilliant epidemiologists are searching for a cure, other researchers are considering how AI can be effectively utilized to simulate and predict how diseases will spread and how diseases will be contained. This is the art of practical AI and the merging of science and technology to predict the needs of a public health crisis worldwide. Part of the science in predicting is the ability to predict in real-time, based on unplanned scenarios or across various internal and external environmental conditions. Our machines must be able to adapt and respond like humans in order to provide more spontaneous and accurate response, especially in times of dire need.