Li Feifei said, “Simple algorithms and a large amount of good data always win in the field of artificial intelligence, including in the future world model domain as well.” Zihan, WeChat Official Account: HyperAI Super Neural. From dry cleaning to the Elizabeth Queen Engineering Award, Li Feifei reverses the Silicon Valley technology myth, focusing on … Read more
I have grown tired of the stable diffusion I was using before, and I am now a well-known artist with thousands of followers on Pixiv. Today, I picked up a new toy, ChatGLM! This is a large-scale bilingual language model with question-and-answer and dialogue capabilities, optimized for Chinese, and is currently in an invitation-only beta … Read more
Hello everyone, this is Hardware Talk! It has been a while since the last update. I wonder if everyone has been learning in the past few months? I believe many of you have! I apologize for not updating related content; it’s not that I didn’t want to, but I have been improving myself. I needed … Read more
📡 Click the blue text above to follow ↑↑↑ 📡Research Background In modern wireless communication systems, channel coding is a key technology to ensure the reliability of data transmission. In 2009, Arikan proposed Polar Codes, which is the first channel coding scheme strictly proven to achieve the Shannon limit under binary discrete memoryless channels, and … Read more
An Overview of ADC Working Principles ►►► Definition ADC, or Analog-to-Digital Converter, is primarily responsible for converting continuously varying analog signals into digital signals, enabling digital systems (such as Central Processing Units (CPUs) and Microcontrollers (MCUs)) to efficiently process and analyze the transmitted information. Analog signals refer to information transmitted through continuously varying physical quantities, … Read more
Note: Regardless of the quantization method, the ultimate goal is to compress data while minimizing precision loss. QAT does not quantize a fully trained model, but simulates low-precision behavior while the model weights are still being updated. QAT integrates quantization effects during model training or fine-tuning. 1) Method: By simulating the effects of low-precision arithmetic … Read more
Next episode preview, a detailed introduction to the scheduling layer in frameworks like vllm and tgi.1. kTransformer proposed by Tsinghua University, flexibly loads expert models onto the CPU during model execution while loading MLA/KVCache onto the GPU.It can deploy the DeepSeek R1 Q4_K_M quantized model (similar to int4 quantization) on 480G memory + 13G video … Read more