Powering The Edge


On-device machine learning (ML) is a phenomenon that has exploded in popularity. Smart devices that are able to make independent decisions, acting on locally generated data, are hailed as the future of compute for consumer devices: on-device processing slashes latency; increases reliability and safety; boosts privacy and security...all while saving on power and cost. Although ML in edge d... » read more

Inference Moves To The Network


Machine-learning inference started out as a data-center activity, but tremendous effort is being put into inference at the edge. At this point, the “edge” is not a well-defined concept, and future inference capabilities will reside not only at the extremes of the data center and a data-gathering device, but at multiple points in between. “Inference isn't a function that has to resid... » read more

Week In Review: Design, Low Power


Processors Arm rolled out a micro neural processing unit that, when combined with its newest microcontroller, can increase machine learning performance by up to 480 times. The company is aiming the MCU and co-processor across a wide swath of applications. Worth noting is that Arm calls its Cortex-M55 an AI-capable processor, rather than a microcontroller, as the lines between the various proce... » read more

Week In Review: Auto, Security, Pervasive Computing


AI/Edge Arm putting AI (artificial intelligence) and machine learning (ML) on the Cortex-M processor by offering IP for a microNPU for Cortex-M. The company says in a press release that it will deliver a 480x uplift in ML performance. The new Cortex-M IP is Arm Ethos-U55 NPU, which Arm says is the industry’s first microNPU (neural processing unit). Arm is hoping the new IP will start an expl... » read more

A New Dawn For IP


The IP industry is changing again. The concept started as build once, use everywhere, but today it is more like architect once, customize everywhere. Few designs can afford sub-optimal IP for their application. The need for customized IP is driven by both leading-edge designs and the trailing markets, although for different reasons. While this customization is causing IP companies to transfo... » read more

Using FPGAs For AI


Artificial intelligence (AI) and machine learning (ML) are progressing at a rate that is outstripping Moore's Law. In fact, they now are evolving faster than silicon can be designed. The industry is looking at all possibilities to provide devices that have the necessary accuracy and performance, as well as a power budget that can be sustained. FPGAs are promising, but they also have some sig... » read more

Making Sense Of Inferencing Options


Ian Bratt, fellow in Arm’s machine learning group, sheds light on all the different processing elements in machine learning, how different end user requirements affect those choices, why CPUs are a critical element in orchestrating what happens in these systems, and how power and software play into these choices. » read more

Scalable Platforms For Evolving AI


Wear and tear on big, heavy vehicles such as trains can cause unexpected delays and repairs, not to mention create safety hazards that can go unnoticed for months until they become critical. In the past, maintenance teams personally examined the undercarriage of a locomotive to look for stress cracks and other anomalies. Later, imaging and sonar technologies were introduced to find what the hum... » read more

Making Sense Of ML Metrics


Steve Roddy, vice president of products for Arm’s Machine Learning Group, talks with Semiconductor Engineering about what different metrics actually mean, and why they can vary by individual applications and use cases. » read more

Machine Learning Inferencing At The Edge


Ian Bratt, fellow in Arm's machine learning group, talks about why machine learning inferencing at the edge is so difficult, what are the tradeoffs, how to optimize data movement, how to accelerate that movement, and how it differs from developing other types of processors. » read more

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