Wharton Int Tech   陳俊吉 （Frank Chen ）2024/06/10

華頓國際科技 NanoFusion技術應用於所有NVIDIA GPU，為大量AI晶片高功耗的革命性解決方案

How NanoFusion technology is applied to NVIDIA’s GPUs and a large number of AI chips to reduce power consumption and improve performance

The most noteworthy thing is that NanoFusion technology is a theoretically highly advanced technology that mainly involves controlling nuclear fusion reactions at a very small scale (nanometer level) to produce highly efficient, low-energy consuming energy. Energy supply. Its real-life application is still in the research and conceptual stage, but let us assume that such a technology has been implemented and used in the AI ​​and GPU fields.

1. NanoFusion technology
NanoFusion aims to use nanotechnology to control and initiate nuclear fusion reactions. This reaction is thought to be able to produce large amounts of energy while producing far less waste and by-products than traditional nuclear reactions. If applied to GPUs and AI chips, this technology has the potential to significantly improve performance per watt and significantly reduce energy consumption.

2. Application in GPU and AI chips
- Innovation in energy sources:
 NanoFusion can become a new energy source to directly power GPUs and AI chips. This means that more energy can be stored and delivered within the same physical space while reducing energy losses.

- Improve energy efficiency:
 Since nuclear fusion reactions can provide stable high energy output at the nanometer scale, GPUs and AI chips will be able to operate at higher efficiency. This means that even if power consumption increases, the performance improvement rate will be greater.

3. How to reduce the power consumption of large-scale chips
- Local energy management:
 NanoFusion technology can be used as a local energy source inside each AI chip. Through precise control, the nuclear fusion reaction can only be started when needed to provide high energy, avoiding the loss problems encountered in traditional power transmission.

- Improved heat dissipation management:
 Nanofusion technology may also lead to innovative heat dissipation solutions, because the efficiency of nuclear fusion reactions is much higher than that of current technology, and the heat generated is easier to manage, thereby avoiding overheating and energy waste.

- Efficient parallel processing gains:
 NVIDIA's GPU and AI chips are known for massive parallel processing. Utilizing nanonuclear fusion to provide efficient energy, more processing units can run simultaneously without increasing overall power consumption, further improving performance per watt.

Differences in functions and features between GPUs and general-purpose CPUs in the field of AI and machine learning

1. Differences in parallel processing capabilities
 GPUs have a large number of cores (thousands) and can process many operations in parallel at the same time. This makes it ideal for tasks that require extensive matrix calculations, such as training deep learning models.

 CPUs have a small number of high-performance cores (usually around dozens) and are good at complex sequential processing. It is very efficient in multitasking and performing different types of tasks sequentially.

 example:
 - GPU: NVIDIA Tesla V100 (with 5120 CUDA cores, specially designed for training large-scale AI models)
 - CPU: Intel Core i9-11900K** (8-core 16-thread high-performance processor, compatible with a wide range of uses from general offices to games)

 2. The difference between computing power and usage
 GPU* is extremely powerful in floating-point operations (especially single- and half-precision), making it extremely powerful for deep learning tasks such as convolutional neural networks (CNN).

 The CPU can handle a wide range of computing tasks and has fast cache and advanced branch prediction capabilities, making it suitable for running general applications.

 example:
 - GPU: NVIDIA GeForce RTX 3080 (powerful artificial intelligence inference and instant graphics rendering capabilities)
 - CPU: AMD Ryzen 9 5900X (12 cores 24 threads, great for gaming, content creation, and more)

 3. Memory differences
 The GPU's onboard memory (VRAM) enables high-speed data transfer and is ideal for processing large data sets.

 The CPU uses standard RAM and is highly flexible for general data operations.

example:
 - GPU: NVIDIA A100 (up to 40GB fast HBM2 memory)
 - CPU: Intel Xeon scalable processor (supports large amounts of standard RAM, used in large data centers)

 4. Differences in power consumption and cost
 GPUs generally consume more power and cost more, but they are considered an investment for processing many parallel computing tasks in a short period of time.

CPU power consumption is relatively low and the cost is low, but its performance for parallel computing tasks is poor.

 example:
 - GPU: NVIDIA RTX 3090 (350W power consumption)
 - CPU: Intel Core i5-11600K (125W power consumption)

 - Application-specific: GPUs are designed for parallel processing and are ideal for deep learning and graphics rendering. CPUs are suitable for general-purpose processing and are good at performing common computing tasks.
 - Cost and energy efficiency: GPUs are expensive and power-hungry, but are suitable for specific tasks that require high performance. CPUs are cost-effective and consume less energy. Therefore, it is important to select the appropriate processor based on the characteristics of the required computing tasks.

NVIDIA's GPUs and AI chips do consume more power than traditional CPUs when performing computing and deep learning tasks. This is mainly caused by the following reasons:

1. Computing power and parallel processing:
 - NVIDIA's GPU has thousands of small processing cores that can perform a large number of computing tasks at the same time, which is far more than the few to dozens of cores of traditional CPUs. Running so many cores simultaneously consumes a lot of energy.

2. Deep learning and artificial intelligence workloads:
 - The training and inference process of deep learning models (such as convolutional neural networks and transformer models) requires a large number of matrix multiplications and numerical calculations, which are very energy demanding. These loads are mainly borne by the GPU, so the power consumption of the AI ​​chip will be more significant.

3. High storage bandwidth and fast access requirements:
 - In order to achieve efficient performance, especially when processing large amounts of data, high-bandwidth and high-speed access memories such as GDDR6 and HBM are required. These high-performance memories also consume less power.

Nonetheless, the technological advancements mentioned by Jen-Hsun Huang have resulted in overall performance improvements and enhanced energy efficiency. Even if the power consumption of the AI ​​chip itself increases, compared with the performance improvement it brings, the overall computing resource usage efficiency and economy have been greatly improved. Specifically:

1. Parallel computing efficiency:
 - The parallel computing capability of the GPU enables a large number of tasks to be completed in a short period of time. Although the power consumption per unit time increases, due to the increase in computing speed, the total power consumption time is shortened, thereby improving the overall energy efficiency.

 2. Total power efficiency:
 - As Huang Renxun said, up to a hundred times acceleration can be achieved, while the power consumption only increases three times, which means that the performance per watt is greatly improved. In this case, when considering the total power consumption, it will be found that the overall energy efficiency is significantly improved, even if the instantaneous power consumption of a single chip increases.

3. Overall cost savings:
 - Huida's technology enables the entire system to use fewer resources to complete more work when performing complex calculations. The cost savings and power consumption balance the increase in power consumption of the chip itself.

4. Integration benefits:
 - When AI systems and GPUs are used in multiple fields on a large scale, better overall benefits can be achieved through more efficient resource utilization and workflow optimization.

The increase in AI chip power consumption is mainly due to the significant expansion of computing power and the demand for deep learning work. However, this growth is balanced with overall efficiency and economics, and results in an overall optimization of costs and energy consumption.

Background and conditions

Looking back from 2022 to 2026, Huida is actually doing one thing, "accelerating everything." Huang Renxun said: "As the amount of data that needs to be processed grows exponentially, the CPU expansion speed slows down and will basically stop expanding eventually. Only through Huida's accelerated computing method can costs be reduced. GPUs and CPUs can work in parallel. , can achieve up to a hundred times acceleration, while only increasing power consumption by three times, and the performance per watt is 25 times higher than using a CPU alone. Huida's technology can save 98% of costs and reduce power consumption by 97%. , that is, the more you buy, the more you save.”

The next wave of AI is physical robots
Huang Renxun believes that the next wave of AI is physical AI, and robots are one of the ways to express physical AI. Physical AI requires stronger simulation capabilities. Robots complete training and learning in the virtual world, and then enter the factory, and the factory’s command system Robots will be coordinated to manufacture robotic products. In the consumer electronics sector, Huida's RTX AI PC is powered by RTX technology and plans to completely change the consumer experience through more than 200 RTX AI laptops and more than 500 AI-driven applications and games. Huida's technology can save 98% of costs and reduce power consumption by 97%, that is, the more you buy, the more you save. "

 Wharton Int Tech by Frank Chen 日期:2024/06/10