Digital neuromorphic processors are emerging as a promising computing substrate for low-power, always-on EdgeAI applications. In this tutorial paper, we outline the main architectural design principles behind fully digital neuromorphic processors and illustrate them using the SENECA platform as a running example. Starting from a flexible array of tiny RISC-V processing cores connected by a simple Network-on-Chip (NoC), we show how to progressively evolve the architecture: from a baseline event-driven implementation of fully connected networks, to versions with dedicated Neural Processing Elements (NPEs) and a loop controller that offloads fine-grained control from the general-purpose cores. Along the way, we discuss software and mapping techniques such as spike grouping, event-driven depth-first convolution for convolutional networks, and hard-attention style processing for high-resolution event-based vision. The focus is on architectural trade-offs, performance and energy bottlenecks, and on leveraging flexibility to incrementally add domain-specific acceleration. This paper assumes familiarity with basic neuromorphic concepts (spikes, event-driven computation, sparse activation) and deep neural network workloads. It does not present new experimental results; instead, it synthesizes and contextualizes findings previously reported in our SENECA publications to provide a coherent, step-by-step architectural perspective for students and practitioners who wish to design their own digital neuromorphic processors.

The Snapdragon Tech Summit is a multi-day event that showcases the latest mobile technology Qualcomm has to offer. This is the second year that Qualcomm has held simultaneous events in China and Hawaii, as well as streaming the keynote addresses. Day 1 of the Snapdragon Tech Summit kicked off with the introduction of the latest smartphone system-on-chip (SoC) for smartphones – the Snapdragon 8 Gen 2. As expected, it delivers improvements in performance and efficiency for camera, connectivity, gaming, sound, and security. But the biggest punch comes from the use of artificial intelligence (AI) in just about every area. The company went so far as to call it "purpose built for AI." Qualcomm uses all of the Snapdragon SoC's processing elements for AI processing and calls the combination of these processing elements the "AI engine."

In this paper, an online task scheduling and mapping method based on a fuzzy neural network (FNN) learned by an evolutionary multi-objective algorithm (NSGA-II) to jointly optimize the main design challenges of heterogeneous MPSoCs is proposed. In this approach, first, the FNN parameters are trained using an NSGA-II-based optimization engine by considering the main design challenges of MPSoCs including temperature, power consumption, failure rate, and execution time on a training dataset consisting of different application graphs of various sizes. Next, the trained FNN is employed as an online task scheduler to jointly optimize the main design challenges in heterogeneous MPSoCs. Due to the uncertainty in sensor measurements and the difference between computational models and reality, applying the fuzzy neural network is advantageous in online scheduling procedures. The performance of the method is compared with some previous heuristic, meta-heuristic, and rule-based approaches in several experiments. Based on these experiments our proposed method outperforms the related studies in optimizing all design criteria. Its improvement over related heuristic and meta-heuristic approaches are estimated 10.58% in temperature, 9.22% in power consumption, 39.14% in failure rate, and 12.06% in execution time, averagely. Moreover, considering the interpretable nature of the FNN, the frequently fired extracted fuzzy rules of the proposed approach are demonstrated.

Imagine earning your engineering degree in 50 minutes? Sandia National Laboratories has developed a new machine-learning algorithm capable of performing simulations for materials scientists nearly 40,000 times faster than normal, according to a Sandia press release. Their results, published in the January issue of a journal called npj Computational Materials, could herald a dramatic acceleration in the development of new technologies for optics, aerospace, energy storage, and potentially medicine while simultaneously saving laboratories money on computing costs, according to the study. The research, funded by the U.S. Department of Energy's Basic Energy Sciences program, was conducted at the Center for Integrated Nanotechnologies, a Department of Energy user research facility jointly operated by Sandia and Los Alamos national labs. Sandia researchers used machine learning to accelerate a computer simulation that predicts how changing a design or fabrication process, such as tweaking the amounts of metals in an alloy, will affect a material.

New types of AI chips that adopt different ways of organizing memory, compute and networking could reshape the way leading enterprises design and deploy AI algorithms. At least one vendor, Cerebras Systems, has begun testing a single chip about the size of an iPad that moves data around thousands of times faster than existing AI chips. This could open opportunities for developers to experiment with new kinds of AI algorithms. "This is a massive market opportunity and I see a complete rethink of computer architecture in progress," said Ashmeet Sidana, chief engineer at Engineering Capital, a VC firm. The rethink is long overdue, Sidana noted.

Microsoft rose to dominance during the '80s and '90s thanks to the success of its Windows operating system running on Intel's processors, a cosy relationship nicknamed "Wintel". Now Microsoft hopes that another another hardware–software combo will help it recapture that success--and catch rivals Amazon and Google in the race to provide cutting-edge artificial intelligence through the cloud. Microsoft hopes to extend the popularity of its Azure cloud platform with a new kind of computer chip designed for the age of AI. Starting today, Microsoft is providing Azure customers with access to chips made by the British startup Graphcore. Graphcore, founded in Bristol, UK, in 2016, has attracted considerable attention among AI researchers--and several hundred million dollars in investment--on the promise that its chips will accelerate the computations required to make AI work.

Deep learning is all the rage these days in enterprise circles, and it isn't hard to understand why. Whether it is optimizing ad spend, finding new drugs to cure cancer, or just offering better, more intelligent products to customers, machine learning -- and particularly deep learning models -- have the potential to massively improve a range of products and applications. The key word though is'potential.' While we have heard oodles of words sprayed across enterprise conferences the last few years about deep learning, there remain huge roadblocks to making these techniques widely available. Deep learning models are highly networked, with dense graphs of nodes that don't "fit" well with the traditional ways computers process information.

Are two heads better than one? So it seems, especially when one "head" belongs to a machine with artificial intelligence. Bringing human cognition and AI together is the hallmark of the Fifth Industrial Revolution, an era, coming soon, in which people and robots will work collaboratively to the benefit of both. Industry 5.0 will push computing beyond the edge to a world in which humans thrive as never before--because of, not in spite of, our robot companions. Like in the current industrial revolution, Industry 4.0, in Industry 5.0 everything--people, objects, computing devices--will be connected in a vast digital web in which humans seem almost superfluous.

Artificial Intelligence (AI) and Machine Learning (ML) are changing everything in the electronics industry. Engineers are now evaluating how to design and train intelligence solutions in everything from sensors, to smartphones, to networks, to cloud data centers. However, just as there has never been a single processor for every application or workload, so too is there no single solution for AI. Qualcomm appears to be hedging its bets with a distributed AI solution the company calls the Artificial Intelligence Engine (AIEngine) and a dedicated AI accelerator, that was just announced at a company sponsored event in China. At the moment, most of the training of artificial neural networks is done in data centers.

One of the lesser-known projects being pursued by the National Aeronautics and Space Administration is the development of software that learns automatically to find patterns in scientific data. Now, the project could get its computer hardware from an oddly familiar source: the agency's former chief, Dan Goldin, who founded a startup making chips to better handle those calculations The company, KnuEdge, has modeled its computer chip on the human brain in an attempt to increase the speed of programs that fall under the umbrella of machine learning. The new chip could be plugged into data centers to teach itself such jobs as sorting images, understanding language, and following trends in streams of data. Goldin founded the company in 2005, keeping its operations secret until he revealed the new chip on Monday morning, along with voice recognition software that excels in noisy environments. Over the last 10 years, he has supervised the slow process of building the new chip from scratch.