——By 2026, shipments will surpass GPUs, and the edge battlefield will determine the outcome 1. Critical Point: The Countdown to ASIC’s ‘Overtake’ “When Tesla’s autonomous driving smoothly navigates through heavy rain, and when a smartphone camera instantly removes bystanders from photos—behind it all are ASIC chips working silently.” 2026 will become a historical turning point … Read more
Welcome to the True Deep Dive community (click) ← Don’t miss any snippets. If GPUs are like “off-the-rack suits,” then ASICs are like “bespoke tuxedos.” The former can meet the needs of most occasions, but the latter fits perfectly—just like ASICs are tailored computing circuits for specific AI models. When Google launched its first TPU … Read more
AI applications on terminal devices (mobile phones, cars, robots, etc.) are divided into training and inference stages. Training is generally done on servers using NVIDIA graphics cards, which are still a reliable way to obtain ultra-large computing power. The models obtained from training are deployed to terminal devices, which use information captured by sensors such … Read more
Focus on Model Compression, Low-Bit Quantization, Mobile Inference Acceleration Optimization, and Deployment Introduction: This issue contains 15 items. 【News】Shanghai Zhangjiang – News from several GPGPU companies: BoHan released cloud AI inference chip performance exceeding T4 with mass production expected in Q4 this year; Suipian released the largest AI chip in China, Birun’s first 7nm GPU … Read more
The Hexagon NPU (Neural Processing Unit) is a dedicated hardware module designed by Qualcomm to meet the computational demands of artificial intelligence (AI). It is specifically built for low power and high performance AI inference tasks, playing a crucial role in generative AI applications on the edge (such as smartphones, PCs, and other devices). The … Read more
Introduction In today’s natural language processing field, embedding technology has become an indispensable part. Whether for text classification, sentiment analysis, or information retrieval, high-quality embedding models can significantly enhance task performance. However, as model sizes continue to grow, efficiently loading and inferring these models has become a pressing issue. Today, we will introduce a Rust … Read more
Click the blue text above to follow for more exciting content. This article has a total of 697 words, and reading it will take about 2 minutes. ① FPGA Has a Strong Latency Advantage in AI Inference: The “Batch-less” architecture of FPGA provides a significant latency advantage in AI inference. Due to network conditions and … Read more
👆If you would like to meet regularly, feel free to star 🌟 and bookmark it~Source: This article is translated from Zach, thank you. In late April, one of the most well-funded AI chip startups, SambaNova Systems, significantly deviated from its original goals. Like many other AI chip startups, SambaNova initially aimed to provide a unified … Read more
The Xiangteng NPU chip is an AI chip designed for inference.When applying artificial intelligence, we encounter two concepts: Training and Inference, which are two stages in the implementation of AI. Let’s first understand these two issues. What is the difference between training and inference? What key points should we focus on to distinguish between AI … Read more
EETOP focuses on chips and microelectronics, click the blue text above to follow us Yesterday, Arm launched two new IPs (Cortex-M55 and Ethos-U55) to expand its AI-related product offerings. Cortex-M55 The Cortex-M55 CPU brings many new features announced by Arm over the past year. The first new feature is support for custom instructions. Arm first … Read more