[LINK] Analog Iterative Machines (AIM)

[Stephen Loosley]

Microsoft Research Blog

Unlocking the future of computing: The Analog Iterative Machine, with lightning-fast approachs to optimization

Published June 27, 2023  By Hitesh Ballani , Partner Researcher


Analog Iterative Machines (AIM)

Picture a world where computing is not limited by the binary confines of zeros and ones, but instead, is free to explore the vast possibilities of continuous value data.

Over the past three years a team of Microsoft researchers has been developing a new kind of analog optical computer that uses photons and electrons to process continuous value data, unlike today’s digital computers that use transistors to crunch through binary data.

This innovative new machine has the potential to surpass state-of-the-art digital technology and transform computing in years to come.

The Analog Iterative Machine (AIM) is designed to solve difficult optimization problems, which form the foundation of many industries, such as finance, logistics, transportation, energy, healthcare, and manufacturing. However, traditional digital computers struggle to crack these problems in a timely, energy-efficient and cost-effective manner.

This is because the number of possible combinations explodes exponentially as the problem size grows, making it a massive challenge for even the most powerful digital computers. The Traveling Salesman Problem is a classic example. Imagine trying to find the most efficient route for visiting a set of cities just once before returning to the starting point. With only five cities, there are 12 possible routes – but for a 61-city problem, the number of potential routes surpasses the number of atoms in the universe.

AIM addresses two simultaneous trends. First, it sidesteps the diminishing growth of computing capacity per dollar in digital chips – or the unraveling of Moore’s Law.

Second, it overcomes the limitations of specialized machines designed for solving optimization problems.

Despite over two decades of research and substantial industry investment, such unconventional hardware-based machines have a limited range of practical applications, because they can only address optimization problems with binary values.

This painful realization within the optimization community has driven the team to develop AIM, with a design that combines mathematical insights with cutting-edge algorithmic and hardware advancements. The result? An analog optical computer that can solve a much wider range of real-world optimization problems while operating at the speed of light, offering potential speed and efficiency gains of about a hundred times.

Today, AIM is still a research project, but the cross-disciplinary team has recently assembled the world’s first opto-electronic hardware for mixed – continuous and binary – optimization problems.

Though presently operating on a limited scale, the initial results are promising, and the team has started scaling up its efforts. This includes a research collaboration with the UK-based multinational bank Barclays to solve an optimization problem critical to the financial markets on the AIM computer. Separate engagements are aimed at gaining more experience in solving industry-specific optimization problems.

In June 2023, the team launched an online service that provides an AIM simulator to allow partners to explore the opportunities created by this new kind of computer.

The technology

Photons possess a remarkable property of not interacting with one another, which has underpinned the internet era by enabling large amounts of data to be transmitted over light across vast distances. However, photons do interact with the matter through which they propagate, allowing for linear operations such as addition and multiplication, which form the basis for optimization applications.

For instance, when light falls on the camera sensor on our smartphones, it adds up the incoming photons and generates the equivalent amount of current. Additionally, data transmission over fiber which brings internet connectivity to homes and businesses relies on encoding zeroes and ones onto light by programmatically controlling its intensity. This scaling of light through light-matter interaction multiplies the light intensity by a specific value – multiplication in the optical domain.

Beyond optical technologies for linear operations, various other electronic components prevalent in everyday technologies can perform non-linear operations that are also critical for efficient optimization algorithms.

Analog optical computing thus involves constructing a physical system using a combination of analog technologies – both optical and electronic – governed by equations that capture the required computation. This can be very efficient for specific application classes where linear and non-linear operations are dominant.

In optimization problems, finding the optimal solution is akin to discovering a needle in an inconceivably vast haystack. The team has developed a new algorithm that is highly efficient at such needle-finding tasks. Crucially, the algorithm’s core operation involves performing hundreds of thousands or even millions of vector-matrix multiplications – the vectors represent the problem variables whose values need to be determined while the matrix encodes the problem itself. These multiplications are executed swiftly and with low energy consumption using commodity optical and electronic technologies.

Thanks to the miniaturization of all these components onto tiny centimeter-scale chips, the entire AIM computer fits into a small rack enclosure. As light travels incredibly fast – 5 nanoseconds per meter – each iteration within the AIM computer is significantly faster and consumes less electricity than running the same algorithm on a digital computer.

Importantly, since the entire problem is embedded into the modulator matrix inside the computer itself, AIM does not require the problem to be transferred back and forth between storage and compute locations. And unlike synchronous digital computers, AIM’s operation is entirely asynchronous. These architectural choices circumvent key historical bottlenecks for digital computers.

Finally, all technologies used in AIM are already prevalent in consumer products with existing manufacturing ecosystems, which paves the way for a viable computing platform, at full scale, if all the technical challenges can be tamed by the team ... (snip)

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