With the rapid development of AI technology, the demand for large language models and generative AI is driving the transformation of computing hardware. However, the high power consumption, high cost, and complexity of high-performance GPUs and CPUs have become the biggest obstacles to the widespread application of AI inference. To solve this problem, startup Sagence AI has launched a disruptive new technology that redefines what is possible in AI inference hardware by simulating in-memory computing architectures.
What is Analog In-Memory Computing?
To put it simply, analog in-memory computing is a technology that combines data storage and computing functions into one. Traditional hardware needs to transfer data back and forth between memory and processors, which consumes power and adds latency. Sagence AI's technology does the calculations directly in the memory cell, eliminating these steps, resulting in significant improvements in energy efficiency and performance.
What are the advantages of Sagence AI?
1. Ultra-high energy efficiency
Compared to the mainstream GPUs currently on the market for AI inference, Sagence's technology consumes up to 10 times less energy.
2. Extremely low cost
The cost of Sagence technology is only 1/20 of that of traditional solutions, which significantly improves the economics of AI inference.
3. Save space
The modular chip design reduces the footprint of the equipment by 20 times, bringing more flexibility to the data center.
4. High performance
When it comes to processing large language models like the Llama2-70B, Sagence's performance is comparable to that of high-end GPUs, but more efficient and environmentally friendly.
Why is this technology important?
Today, AI models are becoming more complex, and computational requirements are soaring. Although traditional hardware is powerful, it has serious power consumption problems. For example, the most powerful GPUs today consume 1,200 watts, which is astronomical compared to traditional home appliances. This not only increases operating costs, but also places a huge burden on the environment.
Figure: Sagence AI: Driving the Future of AI Inference with Innovative Analog Computing (Source: Sagence AI)
Sagence AI has found a breakthrough through analog computing. Compared to digital computing, analog computing has low power consumption, low latency, and can be manufactured using existing and proven technologies. This innovation clears the way for AI inference to be applied at scale in data centers and edge devices.
Future-proof AI inference architecture
Vishal Sariin, Founder and CEO of Sagence, said: "The future of AI requires completely new inference hardware. Existing high-performance equipment is too expensive and power-hungry to meet the needs of large-scale applications. Our goal is to provide an efficient, cost-effective and environmentally friendly solution.”
What's more, Sagence doesn't see AI inference as an ordinary general-purpose computing task, but rather as a mathematically intensive problem. Their hardware design philosophy is closer to how biological neural networks work, providing a natural and efficient solution.
From the data center to the edge device, it's all covered
Whether it's a large language model running in a data center or a computer vision application on edge devices, Sagence's technology is up to the task. This flexibility gives it a wide range of application potential in multiple industries, such as medical, autonomous driving, smart manufacturing, and more.
Sagence AI's innovative analog in-memory computing technology is a bold challenge to traditional AI inference hardware. It makes large-scale AI inference more efficient, affordable, and sustainable. In the future, as this technology develops further, we may see a significant increase in the spread of AI while contributing to the environmental protection of the planet.
