How The Smart Edge Drives Demand For Efficient Chip Design Strategies
Iri Trashanski, Chief Strategy Officer at Ceva, is shaping the future of the Smart Edge with extensive experience across tech sectors. Back in the day, a well-known mantra in the semiconductor industry...
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The AIPC is Reinventing PC Hardware
We first started hearing about AI-enabled PCs (AIPCs) from Microsoft. As a platform, PCs may seem a mature and unpromising market, but add AI and some amazing things can happen. Quickly summarize a...
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NPU IP Architecture Shaped Through Software Insights and Use-Case Analysis
The advent of Neural Processing Units (NPUs) has revolutionized the field of machine learning, enabling the efficient execution of complex mathematical computations required for deep learning tasks. By...
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Challenges in Designing Automotive Radar Systems
Radar is cropping up everywhere in new car designs: sensing around the car to detect hazards and feed into decision making for braking, steering, and parking; in the cabin for driver and occupancy...
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Futureproofing Automotive AI to Manage Lifetime Cost
Cars and trucks are expected to continue their 10– to 20-year lifetimes for the foreseeable future, with corresponding implications for electronics reliability as we already know. More challenging is...
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Oriented FAST and Rotated BRIEF (ORB) Feature Detection Speeds Up Visual SLAM
In the realm of smart edge devices, signal processing and AI inferencing are intertwined. Sensing can require intense computation to filter out the most significant data for inferencing. Algorithms for...
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Partitioning Strategies to Optimize AI Inference for Multi-Core Platforms
Not so long ago, AI inference at the edge was a novelty easily supported by a single NPU IP accelerator embedded in the edge device. Expectations have accelerated rapidly since then. Now we want...
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Living on the Smart Edge: reflecting on a great year at Ceva
As we bid farewell to 2023, it is with great satisfaction that we look at our remarkable achievements. This year has been a transformative one for Ceva, marked by the beginning of a new era for Ceva...
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Bringing Power Efficiency to TinyML, ML-DSP and Deep Learning Workloads
In recent times, the need for real-time decision making, reduced data throughput, and privacy concerns, has moved a substantial portion of AI processing to the edge. This shift has given rise to a...
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Efficiently Packing Neural Network AI Model for the Edge
Packing applications into constrained on-chip memory is a familiar problem in embedded design, and is now equally important in compacting neural network AI models into a constrained storage. In some...
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Transformer Networks Optimized for ChatGPT Mobile
Siri and OK Google were initially a fun introduction to the promise of voice-based control, but we soon realized how carefully we must craft requests to get a useful response. The level of...
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Transformer Models and NPU IP Co-Optimized for the Edge
Transformers are taking the AI world by storm, as evidenced by super-intelligent chatbots and search queries, as well as image and art generators. These are also based on neural net technologies but...
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What is AI Anomaly Detection and Why it needs Explainable AI (XAI)?
Anomaly detection is the process of identifying when something deviates from the usual and expected. If an anomaly can be detected early enough, relevant corrective action can be taken to avoid serious...
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