Generative AI has accelerated the automation of complex digital tasks, and now multimodal perception and reasoning make embodied AI the next frontier. This shift brings adaptive reasoning into the physical realm, enabling natural interaction between humans and intelligent machines, from […]

Voice is quickly becoming the primary interface for consumer and enterprise devices, driven by improved speech models, small language models and the push for on-device processing to reduce latency and protect privacy. Delivering this experience requires always-on voice and audio […]

Physical AI is caught between two computational titans: autoregressive (AR) transformers, which predict discrete action tokens, and diffusion transformers (DiTs), which refine continuous motion trajectories. Both architectures face a “memory wall,” but for different reasons: AR is bottlenecked by the […]

Edge AI has moved beyond choosing a single chip or platform. Teams now face a harder question: how to go from a prototype to a safe, compliant, large-scale deployment—on thousands to millions of devices—without drowning in integration work and life-cycle […]

Most edge vision deployments fail not because of model architecture, but because real-world training data is structurally incomplete. Sampled data can’t cover combinatorial edge cases, forcing perpetual retraining cycles that break embedded deployment, explainability requirements and silicon viability. In this […]

Discover how AMD’s ROCm software stack unlocks data center-class ML performance on embedded integrated GPUs, enabling diverse AI workloads from CNNs and transformers to VLMs and LLMs in power-constrained edge environments. In this technical session, we show how ROCm, HIP […]

This session explores practical strategies for deploying computer vision AI on far-edge devices under strict resource constraints. While highlighting FPGA-specific strengths, such as customizable dataflows, fine-grained quantization control and efficient near-sensor processing, we will also cover techniques applicable across edge […]

Vision-focused edge AI, where CNNs ruled, was compute-bound. However, today’s vision-language models (VLMs) have flipped the script: they feature as many as 70B parameters and 200K+ token contexts and are highly memory-bound, shifting the bottleneck from compute to memory. KV […]

Running computer vision on microcontrollers is becoming practical thanks to MCUs that integrate NPUs, enabling low-latency inference with tight power and cost budgets. In this session, Roboflow Co-Founder and CEO Joseph Nelson will walk through an end-to-end workflow for building […]

As generative AI models rapidly evolve—with increasingly dynamic transformer architectures and diverse framework variations—the challenge of bringing these models to the edge has grown dramatically. Traditional rule-based optimization pipelines can no longer keep pace with models whose structures shift quickly […]