With increasing signal processing requirements in various types of embedded systems, some companies designed chips that combine both a digital signal processor (DSP) and a general purpose processor to address the processing demands. While this suits some high-performance devices where silicon area and power are less of a concern, such devices could be difficult to program and can be power hungry. To address the challenge, Arm has been working on technologies that boost the signal processing and machine learning capabilities in the Arm Cortex-M55 processor. In this white paper we look at how Arm Helium technology compares to features found on traditional DSPs, and some of the fundamental differences between VLIW (Very Long Instruction Word) architecture and the Helium approach to the Cortex-M55 processor’s design. We will also explore how the processing requirements affect the processor’s level-one memory system design and the overall performance benefit of the Helium technology.
- Embedded Security Webinar Series
- Machine Learning
- Power Electronics
- Dev Tools & OS
- Dev Kit Selector
- White Papers
- Upcoming Activities & Events
- Embedded Executive
ONE Tech estimates that MicroAI Atom algorithms, which run recursive analysis and reside directly on target...
We also recommend:
Arm has been working on technologies that boost the signal processing and machine learning capabilities in the Arm Cortex-M55 processor.
One of the major factors of digital transformation is AIoT, which delivers intelligent connected systems that are capable of self-correcting.
Aveva announced the release of its Aveva Insights Operations Management Interface (OMI) app. The application was designed to combine AI with an operator’s real-time decision-making.
NXP combined the target-specific optimization capability of the Glow compiler with Arm Cortex-M and Cadence Tensilica HiFi 4 DSP neural network operator libraries within the eIQ IDE.
As the technology gets pushed out to the Edge of the IoT, the number of uses climbs considerably. Developers are moving quickly toward deployment of their AI architectures.
Deploying artificial intelligence and machine learning at the Edge of the IoT has long been the Holy Grail for design engineers.
Octal flash device incorporating leading embedded AI processing with Renesas’ Dynamically Reconfigurable Processor (DRP) Technology targeted at Industrial IoT market.
Architecture exploration of AI applications is complex and involves multiple studies. To start with, we can target a single problem such as memory access or can look at the full processor or system.
The concept of energy harvesting has been around for over a decade; however, the implementation of ambient energy-powered systems in the real-world environment has been cumbersome, complex and costly.
AI includes a wide range of technologies such as machine learning, deep learning (DL), optical character recognition (OCR), natural language processing (NLP), voice recognition, and so on.
AIoT is the combined effort of the two technologies that have been dominating the field over the past few years (AI and IoT).
ONE Tech estimates that MicroAI Atom algorithms, which run recursive analysis and reside directly on target MCUs at the edge, reduce the cost of deploying endpoint intelligence by a minimum of 80%.
The collaboration with Innominds is expected to drive transformational initiatives in identity management through the combination of facial recognition technologies.
AAEON announced the release of the BOXER-8251AI AI edge box PC powered by NVIDIA® Jetson Xavier™ NX.
ICARVISIONS released its IVMS(web) platform for support of AI and big data.
dSPACE announced it has acquired Intempora, a real-time development software company. The acquisition supports the strengthening of dSPACE’s autonomous driving product portfolio.
Learn more about the new SMARC™ 2.1 specification, new key features and changes. Get a detailed comparison between the SMARC™ 2.0 module and the SMARC™ 2.1 module and find out more about special use..
AI is not only used for path planning and obstacle avoidance, but incorporated in every step of development from modeling how systems will perform on the road to gathering parts for manufacturing.
Challenges faced by IoT deployments related to latency, network bandwidth, reliability, and security cannot be addressed by cloud-only models, so the focus of IoT is moving towards the edge.
Applitools announced its Visual AI technology has surpassed one billion images analyzed since launching commercially and is projected to double year over year.