CEVA Redefines High Performance AI/ML Processing for Edge AI and Edge Compute Devices with its NeuPro-M Heterogeneous and Secure Processor…
- 3rd generation NeuPro AI/ML architecture offers scalable performance of 20 to 1,200 TOPS at SoC and Chiplet levels, lowers memory bandwidth by 6X
- Targets broad use of AI/ML in automotive, industrial, 5G networks and handsets, surveillance cameras, and Edge Compute
LAS VEGAS, Jan. 6, 2022 /PRNewswire/ -- Consumer Electronics Show CEVA, Inc. (NASDAQ: CEVA), the leading licensor of wireless connectivity and smart sensing technologies and integrated IP solutions, today announced NeuPro-M, its latest generation processor architecture for artificial intelligence and machine learning (AI/ML) inference workloads. Targeting the broad markets of Edge AI and Edge Compute, NeuPro-M is a self-contained heterogeneous architecture that is composed of multiple specialized co-processors and configurable hardware accelerators that seamlessly and simultaneously process diverse workloads of Deep Neural Networks, boosting performance by 5-15X compared to its predecessor. An industry first, NeuPro-M supports both system-on-chip (SoC) as well as Heterogeneous SoC (HSoC) scalability to achieve up to 1,200 TOPS and offers optional robust secure boot and end-to-end data privacy.
NeuPro-M is the latest generation processor architecture from CEVA for artificial intelligence and machine learning (AI/ML) inference workloads. Targeting the broad markets of Edge AI and Edge Compute, NeuPro-M is a self-contained heterogeneous architecture that is composed of multiple specialized co-processors and configurable hardware accelerators that seamlessly and simultaneously process diverse workloads of Deep Neural Networks, boosting performance by 5-15X compared to its predecessor.
NeuProM compliant processors initially include the following pre-configured cores:
NPM11 single NeuPro-M engine, up to 20 TOPS at 1.25GHz
NPM18 eight NeuPro-M engines, up to 160 TOPS at 1.25GHz
Illustrating its leading-edge performance, a single NPM11 core, when processing a ResNet50 convolutional neural network, achieves a 5X performance increase and 6X memory bandwidth reduction versus its predecessor, which results in exceptional power efficiency of up to 24 TOPS per watt.
Built on the success of its' predecessors, NeuPro-M is capable of processing all known neural network architectures, as well as integrated native support for next-generation networks like transformers, 3D convolution, self-attention and all types of recurrent neural networks. NeuPro-M has been optimized to process more than 250 neural networks, more than 450 AI kernels and more than 50 algorithms. The embedded vector processing unit (VPU) ensures future proof software-based support of new neural network topologies and new advances in AI workloads. Furthermore, the CDNN offline compression tool can increase the FPS/Watt of the NeuPro-M by a factor of 5-10X for common benchmarks, with very minimal impact on accuracy.
Story continues
Ran Snir, Vice President and General Manager of the Vision Business Unit at CEVA, commented: "The artificial intelligence and machine learning processing requirements of edge AI and edge compute are growing at an incredible rate, as more and more data is generated and sensor-related software workloads continue to migrate to neural networks for better performance and efficiencies. With the power budget remaining the same for these devices, we need to find new and innovative methods of utilizing AI at the edge in these increasingly sophisticated systems. NeuPro-M is designed on the back of our extensive experience deploying AI processors and accelerators in millions of devices, from drones to security cameras, smartphones and automotive systems. Its innovative, distributed architecture and shared memory system controllers reduces bandwidth and latency to an absolute minimum and provides superb overall utilization and power efficiency. With the ability to connect multiple NeuPro-M compliant cores in a SoC or Chiplet to address the most demanding AI workloads, our customers can take their smart edge processor designs to the next level."
The NeuPro-M heterogenic architecture is composed of function-specific co-processors and load balancing mechanisms that are the main contributors to the huge leap in performance and efficiency compared to its predecessor. By distributing control functions to local controllers and implementing local memory resources in a hierarchical manner, the NeuPro-M achieves data flow flexibility that result in more than 90% utilization and protects against data starvation of the different co-processors and accelerators at any given time. The optimal load balancing is obtained by practicing various data flow schemes that are adopted to the specific network, the desired bandwidth, the available memory and the target performance, by the CDNN framework.
NeuPro-M architecture highlights include:
Main grid array consisting of 4K MACs (Multiply And Accumulates), with mixed precision of 2-16 bits
Winograd transform engine for weights and activations, reducing convolution time by 2X and allowing 8-bit convolution processing with <0.5% precision degradation
Sparsity engine to avoid operations with zero-value weights or activations per layer, for up to 4X performance gain, while reducing memory bandwidth and power consumption
Fully programmable Vector Processing Unit, for handling new unsupported neural network architectures with all data types, from 32-bit Floating Point down to 2-bit Binary Neural Networks (BNN)
Configurable Weight and Data compression down to 2-bits while storing to memory, and real-time decompression upon reading, for reduced memory bandwidth
Dynamically configured two level memory architecture to minimize power consumption attributed to data transfers to and from an external SDRAM
To illustrate the benefit of these innovative features in the NeuPro-M architecture, concurrent use of the orthogonal mechanisms of Winograd transform, Sparsity engine, and low-resolution 4x4-bit activations, delivers more than a 3X reduction in cycle count of networks such as Resnet50 and Yolo V3.
As neural network Weights and Biases and the data set and network topology become key Intellectual Property of the owner, there is a strong need to protect these from unauthorized use. The NeuPro-M architecture supports secure access in the form of optional root of trust, authentication, and cryptographic accelerators.
For the automotive market, NeuPro-M cores and its CEVA Deep Neural Network (CDNN) deep learning compiler and software toolkit comply with Automotive ISO26262 ASIL-B functional safety standard and meets the stringent quality assurance standards IATF16949 and A-Spice.
Together with CEVA's multi award-winning neural network compiler CDNN and its robust software development environment, NeuPro-M provides a fully programmable hardware/software AI development environment for customers to maximize their AI performance. CDNN includes innovative software that can fully utilize the customers' NeuPro-M customized hardware to optimize power, performance & bandwidth. The CDNN software also includes a memory manager for memory reduction and optimal load balancing algorithms, and wide support of various network formats including ONNX, Caffe, TensorFlow, TensorFlow Lite, Pytorch and more. CDNN is compatible with common open-source frameworks, including Glow, tvm, Halide and TensorFlow and includes model optimization features like 'layer fusion' and 'post training quantization' all while using precision conservation methods.
NeuPro-M is available for licensing to lead customers today and for general licensing in Q2 this year. NeuPro-M customers can also benefit from Heterogenous SoC design services from CEVA to help integrate and support system design and chiplet development. For further information, visit https://www.ceva-dsp.com/product/ceva-neupro-m/.
About CEVA, Inc.CEVA is the leading licensor of wireless connectivity and smart sensing technologies and integrated IP solutions for a smarter, safer, connected world. We provide Digital Signal Processors, AI engines, wireless platforms, cryptography cores and complementary software for sensor fusion, image enhancement, computer vision, voice input and artificial intelligence. These technologies are offered in combination with our Intrinsix IP integration services, helping our customers address their most complex and time-critical integrated circuit design projects. Leveraging our technologies and chip design skills, many of the world's leading semiconductors, system companies and OEMs create power-efficient, intelligent, secure and connected devices for a range of end markets, including mobile, consumer, automotive, robotics, industrial, aerospace & defense and IoT.
Our DSP-based solutions include platforms for 5G baseband processing in mobile, IoT and infrastructure, advanced imaging and computer vision for any camera-enabled device, audio/voice/speech and ultra-low-power always-on/sensing applications for multiple IoT markets. For sensor fusion, our Hillcrest Labs sensor processing technologies provide a broad range of sensor fusion software and inertial measurement unit ("IMU") solutions for markets including hearables, wearables, AR/VR, PC, robotics, remote controls and IoT. For wireless IoT, our platforms for Bluetooth (low energy and dual mode), Wi-Fi 4/5/6/6e (802.11n/ac/ax), Ultra-wideband (UWB), NB-IoT and GNSS are the most broadly licensed connectivity platforms in the industry.
Visit us at http://www.ceva-dsp.com and follow us on Twitter, YouTube, Facebook, LinkedIn and Instagram.
CEVA - a global leader in signal processing IP for everything smart and connected. (PRNewsFoto/CEVA, Inc.)
Cision
View original content to download multimedia:https://www.prnewswire.com/news-releases/ceva-redefines-high-performance-aiml-processing-for-edge-ai-and-edge-compute-devices-with-its-neupro-m-heterogeneous-and-secure-processor-architecture-301455262.html
SOURCE CEVA, Inc.
- AI and machine learning are the future of Wi-Fi management: WBA report - Telecompetitor - February 22nd, 2026 [February 22nd, 2026]
- Machine learning streamlines the complexities of making better proteins - Science News - February 20th, 2026 [February 20th, 2026]
- WBA Publishes Guidance on Artificial Intelligence and Machine Learning for Intelligent Wi-Fi - ARC Advisory Group - February 20th, 2026 [February 20th, 2026]
- Machine learning-predicted insulin resistance is a risk factor for 12 types of cancer - Nature - February 20th, 2026 [February 20th, 2026]
- Exploring Machine Learning at the DOF - University of the Philippines Diliman - February 20th, 2026 [February 20th, 2026]
- AI and Machine Learning - Where US agencies are finding measurable value from AI - Smart Cities World - February 20th, 2026 [February 20th, 2026]
- Modeling visual perception of Chinese classical private gardens with image parsing and interpretable machine learning - Nature - February 16th, 2026 [February 16th, 2026]
- Analysis of Market Segments and Major Growth Areas in the Machine Learning (ML) Feature Lineage Tools Market - openPR.com - February 16th, 2026 [February 16th, 2026]
- Apple Makes One Of Its Largest Ever Acquisitions, Buys The Israeli Machine Learning Firm, Q.ai - Wccftech - February 1st, 2026 [February 1st, 2026]
- Keysights Machine Learning Toolkit to Speed Device Modeling and PDK Dev - All About Circuits - February 1st, 2026 [February 1st, 2026]
- University of Missouri Study: AI/Machine Learning Improves Cardiac Risk Prediction Accuracy - Quantum Zeitgeist - February 1st, 2026 [February 1st, 2026]
- How AI and Machine Learning Are Transforming Mobile Banking Apps - vocal.media - February 1st, 2026 [February 1st, 2026]
- Machine Learning in Production? What This Really Means - Towards Data Science - January 28th, 2026 [January 28th, 2026]
- Best Machine Learning Stocks of 2026 and How to Invest in Them - The Motley Fool - January 28th, 2026 [January 28th, 2026]
- Machine learning-based prediction of mortality risk from air pollution-induced acute coronary syndrome in the Western Pacific region - Nature - January 28th, 2026 [January 28th, 2026]
- Machine Learning Predicts the Strength of Carbonated Recycled Concrete - AZoBuild - January 28th, 2026 [January 28th, 2026]
- Vertiv Next Predict is a new AI-powered, managed service that combines field expertise and advanced machine learning algorithms to anticipate issues... - January 28th, 2026 [January 28th, 2026]
- Machine Learning in Network Security: The 2026 Firewall Shift - openPR.com - January 28th, 2026 [January 28th, 2026]
- Why IBMs New Machine-Learning Model Is a Big Deal for Next-Generation Chips - TipRanks - January 24th, 2026 [January 24th, 2026]
- A no-compromise amplifier solution: Synergy teams up with Wampler and Friedman to launch its machine-learning power amp and promises to change the... - January 24th, 2026 [January 24th, 2026]
- Our amplifier learns your cabinets impedance through controlled sweeps and continues to monitor it in real-time: Synergys Power Amp Machine-Learning... - January 24th, 2026 [January 24th, 2026]
- Machine Learning Studied to Predict Response to Advanced Overactive Bladder Therapies - Sandip Vasavada - UroToday - January 24th, 2026 [January 24th, 2026]
- Blending Education, Machine Learning to Detect IV Fluid Contaminated CBCs, With Carly Maucione, MD - HCPLive - January 24th, 2026 [January 24th, 2026]
- Why its critical to move beyond overly aggregated machine-learning metrics - MIT News - January 24th, 2026 [January 24th, 2026]
- Machine Learning Lends a Helping Hand to Prosthetics - AIP Publishing LLC - January 24th, 2026 [January 24th, 2026]
- Hassan Taher Explains the Fundamentals of Machine Learning and Its Relationship to AI - mitechnews.com - January 24th, 2026 [January 24th, 2026]
- Keysight targets faster PDK development with machine learning toolkit - eeNews Europe - January 24th, 2026 [January 24th, 2026]
- Training and external validation of machine learning supervised prognostic models of upper tract urothelial cancer (UTUC) after nephroureterectomy -... - January 24th, 2026 [January 24th, 2026]
- Age matters: a narrative review and machine learning analysis on shared and separate multidimensional risk domains for early and late onset suicidal... - January 24th, 2026 [January 24th, 2026]
- Uncovering Hidden IV Fluid Contamination Through Machine Learning, With Carly Maucione, MD - HCPLive - January 24th, 2026 [January 24th, 2026]
- Machine learning identifies factors that may determine the age of onset of Huntington's disease - Medical Xpress - January 24th, 2026 [January 24th, 2026]
- AI and Machine Learning - WEF expands Fourth Industrial Revolution Network - Smart Cities World - January 24th, 2026 [January 24th, 2026]
- Machine-learning analysis reclassifies armed conflicts into three new archetypes - The Brighter Side of News - January 24th, 2026 [January 24th, 2026]
- Machine learning and AI the future of drought monitoring in Canada - sasktoday.ca - January 24th, 2026 [January 24th, 2026]
- Machine learning revolutionises the development of nanocomposite membranes for CO capture - European Coatings - January 24th, 2026 [January 24th, 2026]
- AI and Machine Learning - Leading data infrastructure is helping power better lives in Sunderland - Smart Cities World - January 24th, 2026 [January 24th, 2026]
- How banks are responsibly embedding machine learning and GenAI into AML surveillance - Compliance Week - January 20th, 2026 [January 20th, 2026]
- Enhancing Teaching and Learning of Vocational Skills through Machine Learning and Cognitive Training (MCT) - Amrita Vishwa Vidyapeetham - January 20th, 2026 [January 20th, 2026]
- New Research in Annals of Oncology Shows Machine Learning Revelation of Global Cancer Trend Drivers - Oncodaily - January 20th, 2026 [January 20th, 2026]
- Machine learning-assisted mapping of VT ablation targets: progress and potential - Hospital Healthcare Europe - January 20th, 2026 [January 20th, 2026]
- Machine Learning Achieves Runtime Optimisation for GEMM with Dynamic Thread Selection - Quantum Zeitgeist - January 20th, 2026 [January 20th, 2026]
- Machine learning algorithm predicts Bitcoin price on January 31, 2026 - Finbold - January 20th, 2026 [January 20th, 2026]
- AI and Machine Learning Transform Baldness Detection and Management - Bioengineer.org - January 20th, 2026 [January 20th, 2026]
- A longitudinal machine-learning approach to predicting nursing home closures in the U.S. - Nature - January 11th, 2026 [January 11th, 2026]
- Occams Razor in Machine Learning. The Power of Simplicity in a Complex World - DataDrivenInvestor - January 11th, 2026 [January 11th, 2026]
- Study Explores Use of Automated Machine Learning to Compare Frailty Indices in Predicting Spinal Surgery Outcomes - geneonline.com - January 11th, 2026 [January 11th, 2026]
- Hunting for "Oddballs" With Machine Learning: Detecting Anomalous Exoplanets Using a Deep-Learned Low-Dimensional Representation of Transit... - January 9th, 2026 [January 9th, 2026]
- A Machine Learning-Driven Electrophysiological Platform for Real-Time Tumor-Neural Interaction Analysis and Modulation - Nature - January 9th, 2026 [January 9th, 2026]
- Machine learning elucidates associations between oral microbiota and the decline of sweet taste perception during aging - Nature - January 9th, 2026 [January 9th, 2026]
- Prognostic model for pancreatic cancer based on machine learning of routine slides and transcriptomic tumor analysis - Nature - January 9th, 2026 [January 9th, 2026]
- Bidgely Redefines Energy AI in 2025: From Machine Learning to Agentic AI - galvnews.com - January 9th, 2026 [January 9th, 2026]
- Machine Learning in Pharmaceutical Industry Market Size Reach USD 26.2 Billion by 2031 - openPR.com - January 9th, 2026 [January 9th, 2026]
- Noise-resistant Qubit Control With Machine Learning Delivers Over 90% Fidelity - Quantum Zeitgeist - January 9th, 2026 [January 9th, 2026]
- Machine Learning Models Forecast Parshwanath Corporation Limited Uptick - Real-Time Stock Alerts & High Return Trading Ideas -... - January 9th, 2026 [January 9th, 2026]
- Machine Learning Models Forecast Imagicaaworld Entertainment Limited Uptick - Technical Resistance Breaks & Outstanding Capital Returns -... - January 2nd, 2026 [January 2nd, 2026]
- Cognitive visual strategies are associated with delivery accuracy in elite wheelchair curling: insights from eye-tracking and machine learning -... - January 2nd, 2026 [January 2nd, 2026]
- Machine Learning Models Forecast Covidh Technologies Limited Uptick - Earnings Forecast Updates & Small Investment Trading Plans -... - January 2nd, 2026 [January 2nd, 2026]
- Machine Learning Models Forecast Sri Adhikari Brothers Television Network Limited Uptick - Stock Split Announcements & Rapid Wealth Accumulation -... - January 2nd, 2026 [January 2nd, 2026]
- Army to ring in new year with new AI and machine learning career path for officers - Stars and Stripes - December 31st, 2025 [December 31st, 2025]
- Army launches AI and machine-learning career path for officers - Federal News Network - December 31st, 2025 [December 31st, 2025]
- AI and Machine Learning Transforming Business Operations, Strategy, and Growth AI - openPR.com - December 31st, 2025 [December 31st, 2025]
- New at Mouser: Infineon Technologies PSOC Edge Machine Learning MCUs for Robotics, Industrial, and Smart Home Applications - Business Wire - December 31st, 2025 [December 31st, 2025]
- Machine Learning Models Forecast The Federal Bank Limited Uptick - Double Top/Bottom Patterns & Affordable Growth Trading - bollywoodhelpline.com - December 31st, 2025 [December 31st, 2025]
- Machine Learning Models Forecast Future Consumer Limited Uptick - Stock Valuation Metrics & Free Stock Market Beginner Guides - earlytimes.in - December 31st, 2025 [December 31st, 2025]
- Machine learning identifies statin and phenothiazine combo for neuroblastoma treatment - Medical Xpress - December 29th, 2025 [December 29th, 2025]
- Machine Learning Framework Developed to Align Educational Curricula with Workforce Needs - geneonline.com - December 29th, 2025 [December 29th, 2025]
- Study Develops Multimodal Machine Learning System to Evaluate Physical Education Effectiveness - geneonline.com - December 29th, 2025 [December 29th, 2025]
- AI Indicators Detect Buy Opportunity in Everest Organics Limited - Healthcare Stock Analysis & Smarter Trades Backed by Machine Learning -... - December 29th, 2025 [December 29th, 2025]
- Automated Fractal Analysis of Right and Left Condyles on Digital Panoramic Images Among Patients With Temporomandibular Disorder (TMD) and Use of... - December 29th, 2025 [December 29th, 2025]
- Machine Learning Models Forecast Gayatri Highways Limited Uptick - Inflation Impact on Stocks & Fast Profit Trading Ideas - bollywoodhelpline.com - December 29th, 2025 [December 29th, 2025]
- Machine Learning Models Forecast Punjab Chemicals and Crop Protection Limited Uptick - Blue Chip Stock Analysis & Double Or Triple Investment -... - December 29th, 2025 [December 29th, 2025]
- Machine Learning Models Forecast Walchand PeopleFirst Limited Uptick - Risk Adjusted Returns & Investment Recommendations You Can Trust -... - December 27th, 2025 [December 27th, 2025]
- Machine learning helps robots see clearly in total darkness using infrared - Tech Xplore - December 27th, 2025 [December 27th, 2025]
- Momentum Traders Eye Manas Properties Limited for Quick Bounce - Market Sentiment Report & Smarter Trades Backed by Machine Learning -... - December 27th, 2025 [December 27th, 2025]
- Machine Learning Models Forecast Bigbloc Construction Limited Uptick - MACD Trading Signals & Minimal Risk High Reward - bollywoodhelpline.com - December 27th, 2025 [December 27th, 2025]
- Avoid These 10 Machine Learning Project Mistakes - Analytics Insight - December 27th, 2025 [December 27th, 2025]
- Infleqtion Secures $2M U.S. Army Contract to Advance Contextual Machine Learning for Assured Navigation and Timing - Yahoo Finance - December 12th, 2025 [December 12th, 2025]
- A county-level machine learning model for bottled water consumption in the United States - ESS Open Archive - December 12th, 2025 [December 12th, 2025]
- Grainge AI: Solving the ingredient testing blind spot with machine learning - foodingredientsfirst.com - December 12th, 2025 [December 12th, 2025]
- Improved herbicide stewardship with remote sensing and machine learning decision-making tools - Open Access Government - December 12th, 2025 [December 12th, 2025]