In the article “Practical Implementation of Embedded AI: Let Ethos-U NPU Run Your Model,” we attempted to load models and invoke the NPU for hardware acceleration of inference using the M33 core of the i.MX 93 MPU. This MPU also has two A55 cores, and this article utilizes the A55 running Linux to achieve similar … Read more
With the increasing prevalence of automated and assisted driving, the demand for edge computing is also on the rise. How to assist customers in developing AI applications has become a new topic. Last year, WPG introduced the eIQ edge computing solution, for more details please refer to: here. In 2020, we are introducing the new … Read more
Let’s see what Grok has to say: With the rapid development of Artificial Intelligence (AI), Embedded AI has become increasingly important as a key area that applies AI technology to resource-constrained devices (such as IoT devices and smart hardware). From smart homes to autonomous driving, embedded AI is changing the way we live. If you … Read more
With the explosive growth of smart homes, industrial IoT, and wearable devices, deploying AI models on resource-constrained microcontrollers (MCUs) or memory-limited SoCs has become a core challenge for developers. This article systematically reviews the current mainstream model training tools and deployment strategies in light of the latest technological developments in 2025, helping you achieve efficient … Read more
Introduction: Imagine being able to decipher the internal operational status of a target device merely by monitoring its induced current, without any physical contact. This sounds like a plot from a sci-fi movie, but thanks to the rapid advancements in TinyML technology, it has become a reality! The CurrentSense-TinyML project launched by the Santander security … Read more
According to a report from Electronic Enthusiasts (by Zhou Kaiyang), general-purpose computing chips are rarely optimized for AI computations, which is why in many AI applications, we still need traditional AI chips, such as GPUs or ASICs, which are larger in area and power consumption. Due to these limitations, these AI chips are difficult to … Read more
Source: Deep Learning Enthusiast Editor: Deep Learning Natural Language Processing Link: https://blog.csdn.net/shanglianlm/article/details/85019768 This article is about 1500 words, recommended reading time is 5 minutes tensorflow and pytorch are very similar, here we take pytorch as an example. 19 Types of Loss Functions 1. L1 Loss L1Loss Calculates the absolute difference between output and target. torch.nn.L1Loss(reduction='mean') … Read more
Join the professional CV group at Jishi, and interact with 10,000+ visual developers from prestigious institutions like HKUST, Peking University, Tsinghua University, Chinese Academy of Sciences, CMU, Tencent, Baidu, and more! We also provide monthly expert live streams, real project demand connections, valuable information summaries, and industry technical exchanges. Follow the Jishi Platform public account, … Read more
Rockchip has released its first high-performance AI processor, the RK3399Pro, providing a one-stop turnkey solution for the AI field. Its on-chip NPU (Neural Processing Unit) has a computing performance of up to 3.0 TOPs, featuring high performance, low power consumption, and ease of development. The Three Major Features of the RK3399Pro AI Chip: 1. High … Read more
Source: OneFlow This article is approximately 7732 words long and is recommended to be read in 12 minutes. This article introduces the background of Pathways and provides an in-depth analysis. 01. Why Discuss Pathways? In the past two years, TensorFlow has been caught off guard by the rise of PyTorch, and the entire industry is … Read more