2 min read
This e-book explores how Ceva’s scalable NPU and DSP IP portfolio enables the next generation of smart, on-device AI sensing for everything from ultra-low-power TinyML to high-performance Generative AI. It is designed for both engineers looking to solve technical challenges like memory and power efficiency, and executives seeking a proven, future-proof roadmap to accelerate product time-to-market. By detailing a “self-sufficient” architecture that slashes silicon footprints, this guide provides the blueprint for building smarter, more efficient products across the IoT, automotive, and mobile markets.
What You Will Learn
The specific architectural shifts required to move from cloud-based AI to efficient on-device sensing.
Why “self-sufficient” NPU designs are becoming the standard for eliminating power-hungry companion MCUs in TinyML devices.
How to scale a single product roadmap from basic 4 TOPS sensing up to 1000 TOPS for high-end Generative AI workloads.
The role of weight decompression and sparsity in reducing silicon footprint and memory bandwidth by up to 80%.
Methods for balancing NPU and DSP workloads to optimize complex sensor fusion like vision, radar, and SLAM.
Ways to simplify the transition from training to deployment using a unified software stack and standard AI frameworks.
The essential requirements for meeting automotive-grade safety and reliability standards in next-generation SoC designs.
FAQ’s
1. What are the best AI processors for edge vision and smart retail applications?
While many industry leaders rely on power-heavy GPUs, the most efficient edge vision solutions now utilize specialized NPU IP like the Ceva-NeuPro family. This e-book explains how NeuPro-Nano and NeuPro-M provide the high-performance throughput required for smart retail shelf monitoring, inventory tracking, and customer flow analysis—all while staying within the strict power budgets of battery-operated IoT devices.
2. How does CEVA’s IP solve the sensor fusion challenges in autonomous mobile robots (AMR)?
Reliable sensor fusion for autonomous delivery and warehouse robots requires the simultaneous processing of camera, radar, and LiDAR data. This guide highlights how SensPro2 DSPs act as a high-efficiency hub to offload SLAM and vision kernels from the main NPU, providing the deterministic, low-latency performance that autonomous mobile robots need to navigate dynamic environments safely.
3. Why is “self-sufficient” NPU architecture critical for ultra-low power TinyML?
Standard AI accelerators often require a host MCU to manage control code, which drives up power consumption and latency. Our e-book breaks down how a self-sufficient NPU architecture integrates code execution and memory management into a single core, eliminating the need for a companion processor and reducing the total silicon footprint for ultra-low power DSP and sensing tasks.
4. How can developers reduce the memory footprint of on-device Generative AI?
As Generative AI and Transformers move to the edge, memory bandwidth becomes the primary bottleneck. This e-book introduces Ceva-NetSqueeze™, a proprietary technology that enables NPUs to process compressed model weights directly—slashing the memory and silicon footprint by up to 80% without requiring an intermediate decompression stage, making large models viable for compact SoC designs.
Ready to redefine your Edge AI strategy? Download the e-book.
