Ceva Expands Smart Edge IP with TinyML NPUs for AIoT Devices

– Ceva-NeuPro-Nano NPUs deliver optimal balance of ultra-low power and best performance in small area to efficiently execute TinyML workloads in consumer, industrial and general-purpose AIoT products

– Ceva-NeuPro Studio complete AI SDK for the Ceva-NeuPro NPU family supports open AI frameworks including TensorFlow Lite for Microcontrollers and microTVM to simplify the rapid development of TinyML enabled applications

– Optimized NPUs for embedded devices build on Ceva’s market leadership in IoT connectivity and strong expertise in audio and vision sensing to help semiconductor companies and OEMs unlock the potential of edge AI

Ceva, the leading licensor of silicon and software IP that enables Smart Edge devices to connect, sense and infer data more reliably and efficiently, announced that it has extended its Ceva-NeuPro family of Edge AI NPUs with the introduction of Ceva-NeuPro-Nano NPUs. These highly-efficient, self-sufficient NPUs deliver the power, performance and cost efficiencies needed for semiconductor companies and OEMs to integrate TinyML models into their SoCs for consumer, industrial, and general-purpose AIoT products.

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TinyML refers to the deployment of machine learning models on low-power, resource-constrained devices to bring the power of AI to the Internet of Things (IoT). Driven by the increasing demand for efficient and specialized AI solutions in IoT devices, the market for TinyML is growing rapidly. According to research firm ABI Research, by 2030 over 40% of TinyML shipments will be powered by dedicated TinyML hardware rather than all-purpose MCUs. By addressing the specific performance challenges of TinyML, the Ceva-NeuPro-Nano NPUs aim to make AI ubiquitous, economical and practical for a wide range of use cases, spanning voice, vision, predictive maintenance, and health sensing in consumer and industrial IoT applications.

The new Ceva-NeuPro-Nano Embedded AI NPU architecture is fully programmable and efficiently executes Neural Networks, feature extraction, control code and DSP code, and supports most advanced machine learning data types and operators including native transformer computation, sparsity acceleration and fast quantization. This optimized, self-sufficient architecture enables Ceva-NeuPro-Nano NPUs to deliver superior power efficiency, with a smaller silicon footprint, and optimal performance compared to the existing processor solutions used for TinyML workloads which utilize a combination of CPU or DSP with AI accelerator-based architectures. Furthermore, Ceva-NetSqueeze AI compression technology directly processes compressed model weights, without the need for an intermediate decompression stage. This enables the Ceva-NeuPro-Nano NPUs to achieve up to 80% memory footprint reduction, solving a key bottleneck inhibiting the broad adoption of AIoT processors today.

“Ceva-NeuPro-Nano opens exciting opportunities for companies to integrate TinyML applications into low-power IoT SoCs and MCUs and builds on our strategy to empower smart edge devices with advanced connectivity, sensing and inference capabilities. The Ceva-NeuPro-Nano family of NPUs enables more companies to bring AI to the very edge, resulting in intelligent IoT devices with advanced feature sets that capture more value for our customers,” said Chad Lucien, vice president and general manager of the Sensors and Audio Business Unit at Ceva. “By leveraging our industry-leading position in wireless IoT connectivity and strong expertise in audio and vision sensing, we are uniquely positioned to help our customers unlock the potential of TinyML to enable innovative solutions that enhance user experiences, improve efficiencies, and contribute to a smarter, more connected world.”

According to Paul Schell, Industry Analyst at ABI Research, “Ceva-NeuPro-Nano is a compelling solution for on-device AI in smart edge IoT devices. It addresses the power, performance, and cost requirements to enable always-on use-cases on battery-operated devices integrating voice, vision, and sensing use cases across a wide array of end markets. From TWS earbuds, headsets, wearables, and smart speakers to industrial sensors, smart appliances, home automation devices, cameras, and more, Ceva-NeuPro-Nano enables TinyML in energy constrained AIoT devices.”

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The Ceva-NeuPro-Nano NPU is available in two configurations – the Ceva-NPN32 with 32 int8 MACs, and the Ceva-NPN64 with 64 int8 MACs, both of which benefit from Ceva-NetSqueeze for direct processing of compressed model weights. The Ceva-NPN32 is highly optimized for most TinyML workloads targeting voice, audio, object detection, and anomaly detection use cases. The Ceva-NPN64 provides 2x performance acceleration using weight sparsity, greater memory bandwidth, more MACs, and support for 4-bit weights to deliver enhanced performance for more complex on-device AI use cases such as object classification, face detection, speech recognition, health monitoring, and others.

The NPUs are delivered with a complete AI SDK – Ceva-NeuPro Studio – which is a unified AI stack that delivers a common set of tools across the entire Ceva-NeuPro NPU family, supporting open AI frameworks including TensorFlow Lite for Microcontrollers (TFLM) and microTVM (µTVM).

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The Ceva-NeuPro-Nano Key Features

Flexible and scalable NPU architecture 

• Fully programmable to efficiently execute Neural Networks, feature extraction, control code, and DSP code
• Scalable performance by design to meet a wide range of use cases
  • MAC configurations with up to 64 int8 MACs per cycle
• Future proof architecture that supports the most advanced ML data types and operators
  • 4-bit to 32-bit integer support
  • Native transformer computation
• Ultimate ML performance for all use cases using advanced mechanisms
  • Sparsity acceleration
  • Acceleration of non-linear activation types
  • Fast quantization

Edge NPU with ultra-low memory requirements

• Highly efficient, single core design for NN compute, feature extraction, control code, and DSP code eliminates need for a companion MCU for these computationally intensive tasks
• Up to 80% memory footprint reduction via Ceva-NetSqueeze which directly process compressed model weights without the need for an intermediate decompression stage

Ultra-low energy achieved through innovative energy optimization techniques 

• Automatic on-the-fly energy tuning
• Dramatic energy and bandwidth reduction by distilling computations using weight-sparsity acceleration

Complete, Simple to Use AI SDK

• Ceva-NeuPro Studio provides a unified AI stack, with an easy click-and-run experience, for all Ceva-NeuPro NPUs, from the new Ceva-NeuPro-Nano to the powerful Ceva-NeuPro-M
• Fast time to market by accelerating software development and deployment
• Optimized to work seamlessly with leading, open AI inference frameworks including TFLM and µTVM
• Model Zoo of pretrained and optimized TinyML models covering voice, vision and sensing use cases
• Flexible to adapt to new models, applications and market needs
• Comprehensive portfolio of optimized runtime libraries and off-the-shelf application-specific software

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