Using MMA as the acceleration for AI functions, the overall SoC block diagram is shown in the below Figure. The architecture is similar across each Edge AI device in the portfolio, such as …

Apr 2, 2025 · Processing-in-Memory (PIM) architectures offer promising solutions for efficiently handling AI applications in energy-constrained edge environments.

We propose a memory-centric hardware/software co-design optimization to solve these conflicts from multiple directions. Inspired by the approach used in MEMTI for computer vision

Therefore, AI researchers have come up with a number of powerful optimization techniques and tools to optimize AI models. This paper is to dig deep and describe all kinds of model …

Mar 24, 2025 · Discover AI model optimization for edge devices, including hardware tuning, compression, and energy-efficient techniques for faster, smarter AI.

Figure 1 demonstrates the basic block diagram to run HPC applications on edge devices. Parameter search optimization is essential for optimal HPC performance on edge devices, …

Chapter 4 delves into various model optimization techniques crucial for deploying AI models on edge devices such as smartphones, smartwatches, and IoT devices. These optimizations are …

Our results demonstrate significant improvements in memory optimization and inference latency reduction. Specifically, BTSearch achieves up to 12% overall memory optimization, while …

Our experimental results show that HMO can achieve an average inference latency optimization ratio of 20.13 % compared with native PyTorch on six typical DL image representation models …

Apr 27, 2021 · In this paper, we are proposing a methodology that compresses large AI model and improves the inference time such that it can be deployed on edge devices. The accuracy …