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 […]

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 […]

At the embedded edge, choices of language model architectures have profound implications on the ability to meet demanding performance, latency and energy efficiency requirements. In this presentation, we contrast state-space models (SSMs) with transformers for use in this constrained regime. […]

In this talk we’ll present Moonshine, a speech-to-text model that outperforms OpenAI’s Whisper by a factor of five in terms of speed. Leveraging this efficiency, we’ll show how to build a voice interface on a low-cost, resource-constrained Cortex-A SoC using […]

As large language models (LLMs) and vision-language models (VLMs) have quickly become important for edge applications from smartphones to automobiles, chipmakers and IP providers have struggled with how to adapt processor software stacks. In this talk, Expedera’s Ram Tadishetti examines […]

The deployment of neural networks near sensors brings well-known advantages such as lower latency, privacy and reduced overall system cost—but also brings significant challenges that complicate development. These challenges can be addressed effectively by choosing the right solution and design […]