The EdgeCortix Podcast

By Dr. Sakyasingha Dasgupta. The rapid development of artificial intelligence (AI) applications has created enormous demand for high-performance and energy-efficient computing systems. However, traditional homogeneous architectures based on Von Neumann processors face challenges in meeting the requirements of AI workloads, which often involve massive parallelism, large data volumes

By Dr. Sakyasingha Dasgupta. Edge computing will grow exponentially in the next few years, as more and more devices and applications demand low-latency, high-performance, and privacy-preserving computation at the edge of the network. However, one of the biggest challenges facing edge computing is handling the increasing complexity and diversity of data sources and modalities, such

DNA IPとMERAの強力なコンビネーションで、ユーザーがAIアプリケーションを効率的に動かすためのハードウエア固有の特別な知識習得の必要性を減らすことができます。AI推論の話題の多くは、できるだけ早く多くの処理を提供することに焦点が当てられています。データセンターのよう�

By Nikolay Nez. DNA IP and MERA are a potent combination, reducing the hardware-specific knowledge OEMs would otherwise need to power AI applications efficiently. Much of the AI inference conversation focuses on delivering as many operations as quickly as possible. Massive GPU-based implementations can find homes in data centers with practically unlimited power and cooling. However

By Antonio Nevado. Data scientists and others may have concerns moving PyTorch, TensorFlow, and ONNX models to edge AI software applications – MERA makes it easy and is model-agnostic. PyTorch, TensorFlow, and ONNX are familiar tools for many data scientists and AI software developers. These frameworks run models natively on a CPU or accelerated on a GPU, requiring little hardwar

By Jeffrey Grosman. AI inference is a common term - but what is edge AI inference? EdgeCortix provides an answer in terms of workloads, efficiency, and applications. Artificial intelligence (AI) is changing the rules for many applications. Teams train AI models to recognize objects or patterns, then run AI inference using those models against incoming data streams. When size, weigh