In an interview from CES 2025, Vikram Gupta, Chief Product Officer at Synaptics, discusses the company's strategic collaboration with Google to enhance Edge AI processors, emphasizing the importance of context-aware computing and AI-native platforms for improving user experience and efficiency.
Be the first to know.
Get our edge ai weekly email digest.
Tagged with
Edge AI
ORGANIZATIONS. SHAPING THE INDUSTRY.
Berkeley Artificial Intelligence Research
Research
The Berkeley Artificial Intelligence Research (BAIR) Lab brings together UC...
View more
Latest Posts
GPUs excel in parallel processing for graphics and AI training with scalability, while NPUs focus on low-latency AI inference on edge devices, enhancing privacy by processing data locally. Together, they complement each other in addressing different stages of AI workloads efficiently.
In the final chapter of the Edge AI Technology Report: Generative AI Edition explores the technical hurdles organizations face as they attempt to leverage edge-based generative AI. It also examines strategic opportunities for innovation in hardware, deployment configurations, and security measures.
webinar | JAN 15, 2025
Join us for The Rise of Generative AI at the Edge: From Data Centers to Devices webinar on Wednesday, January 15th
Edge AI Technology Report: Generative AI Edition Chapter 3. Advances in model optimization techniques like pruning, quantization, and knowledge distillation pave the way for more efficient deployment of generative AI at the edge.
How BrainChip's Akida™ Edge Box Simplifies ML Deployment
Edge AI Technology Report: Generative AI Edition Chapter 1. Generative AI's demand for real-time insights is driving a shift from cloud to edge computing, enabling faster, local processing on devices and reducing cloud latency and bandwidth constraints.
Discover how AI is optimizing manufacturing by increasing quality control, reducing downtime and boosting efficiency.
Edge computing: a decentralized computing paradigm that processes data closer to the source, reducing latency and enabling more efficient use of resources.
Edge AI is the process of running artificial intelligence (AI) and machine learning (ML) algorithms on computing devices at the periphery of a network, rather than on large cloud servers.
Similar to visual data, collecting and curating sound data that accurately reflects real-world scenarios is a major hurdle in training effective machine learning models.
2 minutes read
Recently we launched new Edge Impulse functionality that shows just one of the powerful ways that LLMs can be utilized on the edge: using GPT-4o to train a 2,000,000x smaller model, that runs directly on device.
4 minutes read
Several factors contribute to the occurrence of retained surgical bodies. One common factor is the hectic nature of surgical environments, where numerous instruments and items are used during procedures.
5 minutes read
The latest large language models (LLMs), like the newly released ChatGPT-4o, are truly astonishing. They are multimodal, meaning they have multiple senses and can interpret text, images, and audio information and respond in a seemingly human-like manner.
3 minutes read
We are proud to announce that synthetic data generation is now available inside the Edge Impulse platform, enabling a new and efficient way to work with LLM-generated images, audio, and voice to enhance your edge AI models.
3 minutes read
