Like the prior Cortex-A77, the Cortex-A78 will consist of what ARM calls its big.LITTLE octacore architecture, with four high-performance A78 cores and four A55 cores optimized for long battery life. ARM said that a Cortex-A78 core running at 3GHz would deliver 20 percent more sustained, single-core performance than the Cortex-A77 core running at 2.6GHz, assuming 1 watt per core. The performance is based on simulated estimates. Alternatively, a phone maker could clock the A78 to consume half the power at the same performance as the A77, Williamson said. ARM believes that the octacore Cortex-A78 layout will require 15 percent less die space than the Cortex-A77, leading to smaller phones.
Semiconductor and software design company Arm is doubling down on edge AI hardware, a market that's expected to be worth $1.15 billion by 2023. It today announced two new AI-capable processors -- the Arm Cortex-M55 and Ethos-U55, a neural processing unit (NPU) -- designed for internet of things (IoT) endpoint devices, alongside supporting software libraries, toolchains, and models. The company claims that the two chips, which are expected to arrive in market in early 2021, together will deliver an uplift of up to 480 times in machine learning performance in certain voice and vision scenarios. "[Machine learning] processing on low-power endpoint devices is critical to realizing the full potential of AI for IoT," wrote the company in press materials. "An extended range of advanced hardware capabilities is required to enable innovation and scale."
Arm has announced details of its latest processors designed for artificial intelligence and machine learning, the Arm Cortex-M55, as well as the first microNPU (Neural Processing Unit), the Ethos-U55 which offer a combined 480x machine learning improvement for microcontrollers. Cortex-M based processors are already powering a vast range of AI products with over 50 billion chips shipped to partners. Arm claims that its latest Cortex-M55 is its most capable AI processor yet and is the company's first Cortex-M processor to be based on the Armv8.1-M Equipped with Arm Helium vector processing technology, the Cortex-M55 offers significantly enhanced energy-efficient, 5x digital signal processing (DSP) performance improvement and 15x machine learning (ML) performance compared to previous Cortex-M generations. In addition, custom instructions will be added to improve processor performance for specific workloads, which is a new feature for Cortex-M series processors.
NXP Semiconductors N.V. announced that it is enhancing its machine learning development environment and product portfolio. Through an investment, NXP has established an exclusive, strategic partnership with Canada-based Au-Zone Technologies to expand NXP's eIQ Machine Learning (ML) software development environment with easy-to-use ML tools and expand its offering of silicon-optimized inference engines for Edge ML. Additionally, NXP announced that it has been working with Arm as the lead technology partner in evolving Arm Ethos-U microNPU (Neural Processing Unit) architecture to support applications processors. NXP will integrate the Ethos-U65 microNPU into its next generation of i.MX applications processors to deliver energy-efficient, cost-effective ML solutions for the fast-growing Industrial and IoT Edge. "NXP's scalable applications processors deliver an efficient product platform and a broad ecosystem for our customers to quickly deliver innovative systems," said Ron Martino, Senior Vice President and General Manager of Edge Processing at NXP Semiconductors.
Arm has unveiled two new IP cores designed to power machine learning in endpoint devices, IoT devices and other low-power, cost-sensitive applications. The Cortex-M55 microcontroller core is the first to use Arm's Helium vector processing technology, while the Ethos-U55 machine learning accelerator is a micro-version of the company's existing Ethos NPU (neural processing unit) family. The two cores are designed to be used together, though they can also be used separately. Enabling AI and machine learning applications on microcontrollers and other cost-sensitive, low-power resource-constrained devices is known as the tinyML sector. With the rise of 5G initiating a trend for more intelligence in endpoint devices, tinyML is expected to grow exponentially into a market that encompasses billions of consumer and industrial systems.