It includes the company's Helium technology, which addresses machine-learning (ML) applications. Arm estimates that by 2022, more than 20% of IoT endpoint devices will have ML support. The new specification also includes new signal-processing debug features as well as reliability, availability, and serviceability (RAS) extensions. The new enhancements can be added to existing Cortex-M4 and Cortex-M7 as well as the new Cortex-M33 and Cortex-M35P. Enhancements can be added individually to new designs, allowing developers to take advantage of features selectively.

Arm first announced Project Trillium machine learning IPs back in February and we were promised we'd be hearing more about the product in a few months' time. Project Trillium is unusual for Arm to talk about because the IP hasn't been finalised yet and won't be finished until this summer, yet Arm made sure not to miss out on the machine learning and AI "hype train" that has happened over the last 8 months in both the semiconductor industry and as well as particularly in the mobile industry. Today Arm details more of the architecture of what Arm now seems to more consistently call their "machine learning processor" or MLP from here on now. The MLP IP started off a blank sheet in terms of architecture implementation and the team consists of engineers pulled off from the CPU and GPU teams. With the MLP Arm set out to provide three key aspects that are demanded in machine learning IPs: Efficiency of convolutional computations, efficient data movement, and sufficient programmability.

The long fly ball in baseball is a thing of beauty. Spectators hold their breath as the ball arcs gracefully into the sky and then hurtles towards the outfield with a uniformed player in pursuit. Sometimes it ends up nestling gracefully in the hollow of an outstretched glove. When it works, it seems like a simple act, but catching success asks a fascinating question: how do baseball outfielders coordinate their run perfectly to take a long fly ball? The answer reveals something about the way our brains work and gives us an insight into the future of machine learning.

A few weeks ago, Arm announced its first batch of dedicated machine learning (ML) hardware. Under the name Project Trillium, the company unveiled a dedicated ML processor for products like smartphones, along with a second chip designed specifically to accelerate object detection (OD) use cases. Let's delve deeper into Project Trillium and the company's broader plans for the growing market for machine learning hardware. It's important to note that Arm's announcement relates entirely to inference hardware. Its ML and OD processors are designed to efficiently run trained machine learning tasks on consumer-level hardware, rather than training algorithms on huge datasets.

These days, just about everyone in the technology industry is talking Artificial Intelligence (AI) and Machine Learning (ML). There's a huge amount of excitement and a rush to be the first to get it right. What you might have noticed in this dialogue is that almost everyone is talking big, powerful, Neural Network accelerators as an essential part of bringing ML to life on your device – and whilst it's true that they have a significant role to play, they're just one part of the story. Early ML was performed in the cloud with very large data sets, making significant processing power an absolute essential, but today – particularly in the mobile and smart device sectors – the focus is shifting to what can be achieved at the edge. There are a number of reasons for this shift, not least latency, reliability and responsiveness – factors that are of considerable importance to the consumer.

Here's a brief roundup of some interesting news from the AI world from the past two weeks, beyond what we've already reported. TL;DR: Deep RL sucks – A Google engineer has published a long, detailed blog post explaining the current frustrations in deep reinforcement learning, and why it doesn't live up to the hype. Reinforcement learning makes good headlines. Teaching agents to play games like Go well enough to beat human experts like Ke Jie fuels the man versus machine narrative. But a closer look at deep reinforcement learning, a method of machine learning used to train computers to complete a specific task, shows the practice is riddled with problems.

Arm has announced a new suite of products aimed at putting advanced machine learning into mobile devices. The chip design giant has unveiled Project Trillium, a new suite of intellectual property including new highly scalable processors that will deliver enhanced machine learning (ML) and neural network (NN) functionality. Focused on the mobile market, it is hoped it will enable a new class of ML-equipped devices with advanced compute capabilities, including state-of-the-art object detection. "The rapid acceleration of artificial intelligence into edge devices is placing increased requirements for innovation to address compute while maintaining a power efficient footprint," said Rene Haas, president of the IP products group at Arm. "New devices will require the high-performance ML and artificial intelligence capabilities these new processors deliver. Combined with the high degree of flexibility and scalability that our platform provides, our partners can push the boundaries of what will be possible across a broad range of devices."

ARM Holdings announced today Project Trillium consisting of a new suite of ARM IP designed from the offset to bring machine learning to edge devices. The new ARM IP suite will include scalable processors designed to deliver enhanced neural network and machine learning functionality with a focus on the mobile market, according to ARM, which also revealed the fact that they'll enable a new class of machine learning-equipped devices that feature advanced computing capabilities. "The rapid acceleration of artificial intelligence into edge devices is placing increased requirements for innovation to address compute while maintaining a power efficient footprint. To meet this demand, Arm is announcing its new ML platform, Project Trillium," said Rene Haas, president, IP Products Group, Arm. The company said that the high-performance AI and machine learning capabilities, as well as the advanced scalability and flexibility of its new processors developed as part of Project Trillium are required by new devices and will open the door to the development of more advanced smart devices.

Inference will increasingly take place in apps on smartphones and other "edge" devices. While most phones have chips that can process rudimentary neural nets, additional performance beyond the CPU and GPU is needed for images and language processing. As a result, Huawei's latest Kirin 970 has what it calls a Neural Processing Unit, I believe supplied by Tensilica LLC. The iPhone X has the A11X Bionic chip with a custom silicon block for neural network processing to enable face detection and portrait photography with promises to do more in the future. The Qualcomm Snapdragon 835 accelerates TensorFlow, Caffe, Caffe2, MxNet and Android NNAPI across its CPU, GPU, and most importantly, its DSP.

ARM is unveiling its ambitious new machine learning processor platform, dubbed Project Trillium. The platform includes processors and sensors for improving artificial intelligence operations in mobile devices at the edge of networks, rather than in data centers. ARM has created a high-end processor to handle machine learning calculations, or those that enable computers to learn without explicitly being programmed to perform certain tasks. "Project Trillium is a whole new class of product with hardware and software," said Jem Davies, vice president, fellow, and general manager of ARM's Machine Learning Group. "We looked at GPUs (graphics processing units) and CPUs (central processing units), but it became clear that executing with the best efficiency required a ground-up design specific to machine learning."