Modern edge devices demand heterogeneous AI architectures that can mix and match subsystems to accelerate different aspects ...

Model quantization bridges the gap between the computational limitations of edge devices and the demands for highly accurate models and real-time intelligent applications. The convergence of ...

The AI landscape is taking a dramatic turn, as small language and multimodal models are approaching the capabilities of larger, cloud-based systems. This acceleration reflects a broader shift toward ...

The rapid advancements in AI have brought powerful large language models (LLMs) to the forefront. However, most high-performing models are massive, compute-heavy, and require cloud-based inference, ...