The future of artificial intelligence: the revolutionary impact of optical matrix multiplication

The current world of artificial intelligence is power-hungry and computationally limited. The trajectory of model development is rapid, but with this advancement comes the need for substantial increases in computing power. Existing transistor-based computing is approaching its physical limits and is already struggling to meet these growing computing demands.

Large enterprises have tried to solve this problem by developing their own custom chip solutions. However, hardware bottlenecks may be too severe to be overcome with traditional electronic processors. So, how can technology adequately meet the exponential growth in demand for computing power?

Matrix Multiplication

In large language models, more than 90% of computing tasks use matrix multiplication. Matrix multiplication can support various functional modules of artificial intelligence by performing the basic operations of multiplication and addition in a structured manner. This applies not only to language models, but is also the basis of almost all neural networks: it can realize connections between large-scale neurons, perform convolution operations for image classification and object detection, process sequential data, etc. While it is a simple concept, it is critical to efficiently manipulating and transforming data that supports artificial intelligence and countless other applications, so the importance of matrix multiplication cannot be overestimated as artificial intelligence models As it becomes larger and larger, we have to perform more matrix operations, which means we need more powerful computing power. In order to meet this demand, even now, electronic products have reached their limits. Are there any other solutions?

Optical Matrix Multiplication

Optics has been used in many ways to change our lives, most notably in optical communications in fiber optic networks. Optical computing is a natural next step. Digital electronics requires large numbers of transistors to perform the simplest arithmetic operations, while optical computing exploits the laws of physics to perform calculations. Input information is encoded into light beams, and matrix multiplications are performed using the natural properties of optics such as interference and diffraction. Information can be encoded in multiple wavelengths, polarizations, and spatial modes, allowing for an unlimited amount of parallel processing and calculations occurring virtually at the speed of light.

Adding new dimensions through 3D optics

With Dennard scaling and Moore’s law coming to an end, it’s time to revisit the basics of computing. Digital electronics are inherently limited to “2D” layouts—transistor gates and circuits are fabricated on wafers, and computation is performed by the flow of information between different units on the 2D plane. This 2D computing architecture requires ever-increasing transistor density, causes severe interconnect issues, and suffers from the notorious memory bottleneck. With the development of 3D stacked memory, the transformation of 2D design has now begun, but the industry as a whole still has a long way to go to adapt.

Now, optics can revolutionize the game by performing calculations naturally in 3D space. Adding new dimensions can relax many of the limitations in traditional computing. Interconnecting components is easier and more energy efficient, and it allows for ever-increasing throughput (how many computations can be performed in a given time) without compromising latency (how quickly each computation is performed). This is completely unique to 3D optics: whether you're multiplying 10 numbers or 10,000 numbers, it all happens simultaneously at the speed of light. This has a huge impact on the scalability of optical processors, enabling them to reach 1,000 times the speed of current digital processors.

In addition to the inherent scalability of 3D optics, the clock speeds of the optics can provide speeds up to 100 times faster than traditional electronics, and the ability to multiplex wavelengths opens the door to further improvements of up to 100 times. Combining this all together enables exponentially scaling computing speeds with higher throughput, lower latency and improved reliability that only 3D optical matrix multiplication can provide

What does it mean for artificial intelligence?

Regardless of the application, matrix multiplication forms the backbone of all artificial intelligence calculations. Notably, the high throughput and low latency brought by 3D optics are particularly valuable for artificial intelligence inference tasks in the data center, an application driven by real-time responsiveness and efficiency.

3D optical computing offers significant improvements in bandwidth, latency, speed and scalability compared to traditional electronics or integrated photonics. Additionally, it is compatible with existing machine learning algorithms and therefore has the potential to revolutionize all artificial intelligence applications

The above is the detailed content of The future of artificial intelligence: the revolutionary impact of optical matrix multiplication. For more information, please follow other related articles on the PHP Chinese website!