The integration of discrete AI accelerators with edge processors is poised to revolutionize the capabilities of edge computing, enabling real-time, low-latency and energy-efficient AI applications. As the computational power required for complex AI workloads increases, edge computing applications processors can […]
In this presentation, we’ll provide practical guidance on overcoming key challenges in deploying AI at the edge, including remotely managing containerized models in resource-constrained environments using scalable, purpose-built infrastructure. We’ll also cover selecting and integrating the right hardware and software […]
When building AI-powered embedded vision solutions, training and deploying AI models is just the beginning. Once deployed, engineers must continuously monitor model behavior to ensure expected performance. However, real-time visibility into model accuracy and behavior in production remains a major […]
Embedded vision applications, with their demand for ever more processing power, are driving up the size and complexity of edge SoCs. Heterogeneous architectures that bring together CPUs, GPUs and specialized NPUs have become the standard approach to achieving high-performance density […]
Artificial Intelligence is rapidly integrating into every aspect of technology. While the neural processing unit (NPU) often receives the majority of the spotlight as the ultimate AI problem solver, it is essential to recognize that not all AI workloads can […]
Multimodal LLMs promise to bring exciting new abilities to devices! As we see foundational models become more capable, we see compute requirements grow as well. It is not uncommon to see LLMs grow to tens of billions of parameters, at […]
Many computer vision projects reach proof of concept but stall before production due to high costs, deployment challenges and infrastructure complexity. This session explores the path from prototype to production, focusing on how to reduce the cost of vision workloads […]
Large language models (LLMs) often demand hand-coded conversion scripts for deployment on each distinct processor-specific software stack—a process that’s time-consuming and prone to error. In this session, we introduce a model-agnostic approach designed to streamline LLM deployment, especially for the […]
The adoption of large language models (LLMs) and vision-language models (VLMs) in edge AI applications has been limited by on-device computation and memory resources, and also by the cost of training and developing large models suitable for edge applications. The […]
Quadric’s Chimera general-purpose neural processor executes complete AI/ML graphs—all layers, including pre- and post-processing functions traditionally run on separate DSP processors. To enable this, the Chimera Graph Compiler processes and optimizes a combination of NN graphs, Python code and C++ […]
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