The discoveries in this paper show that Intelligence Processing Units (IPUs) offer a viable accelerator alternative to GPUs for machine learning (ML) applications within the fields of materials science and battery research. We investigate the process of migrating a model from GPU to IPU and explore several optimization techniques, including pipelining and gradient accumulation, aimed at enhancing the performance of IPU-based models. Furthermore, we have effectively migrated a specialized model to the IPU platform. This model is employed for predicting effective conductivity, a parameter crucial in ion transport processes, which govern the performance of multiple charge and discharge cycles of batteries. The model utilizes a Convolutional Neural Network (CNN) architecture to perform prediction tasks for effective conductivity. The performance of this model on the IPU is found to be comparable to its execution on GPUs. We also analyze the utilization and performance of Graphcore's Bow IPU. Through benchmark tests, we observe significantly improved performance with the Bow IPU when compared to its predecessor, the Colossus IPU.

Microsoft's Azure Cloud Platform customers now have access to microchips made by Graphcore. Graphcore, a British startup founded in 2016, has put the spotlight (and several millions of dollars in investment capital) on its newest baby: Intelligent Processing Unit microchips (IPU), specifically designed to work with AI. Unlike most AI compatible chips that were designed with a specific programming in mind, Graphcore's IPUs were designed specifically to support the calculations that assist machines in facial recognition technologies, speech recognition, parse language, car automation, and machine learning. Microsoft's posted benchmarks for the IPU match or exceed the performance levels of the current top AI chips available, and Graphcore's code is rumored to perform image-processing tasks several times faster than their opponents. Graphcore's IPU chip also has memory space to eliminate having to move data on and off the chip for processing.

"What we heard universally was that current hardware was holding developers back," says Nigel Toon, co-founder of Graphcore, the Bristol-based startup behind a new chip to help speed up the process-hogging, resource-intensive deployment of AI. By using cloud computing and vast datasets, some neural networks function sufficiently well. The more powerful AI systems in development, however, struggle to process complex rapid-fire calculations at speed if using computer processing units (CPUs) which work sequentially. Latency, in other words, has slowed. "For 70 years we have programmed computers to work on instructions step-by-step," says Toon, 54.

For most organisations, the process of cognification is the single biggest challenge of the next five years. Everything will get smarter – in theory. The limitations of existing computer chips, however, is slowing down the process. Put simply, today's technology simply isn't up to the job. "What we heard universally was that current hardware was holding developers back," says Nigel Toon, co-founder of Graphcore, the Bristol-based startup behind a new chip to help speed up the process-hogging, resource-intensive deployment of AI.

Graphcore has today announced a $50 million Series C funding round by Sequoia Capital as the machine intelligence company prepares to ship its first Intelligence Processing Unit (IPU) products to early access customers at the start of 2018. The Series C round enables Graphcore to significantly accelerate growth to meet the expected global demand for its machine intelligence processor. The funding will be dedicated to scaling up production, building a community of developers around the Poplar software platform, driving Graphcore's extended product roadmap, and investing in its Palo Alto-based US team to help support customers. Nigel Toon, CEO at Graphcore said: "Efficient AI processing power is rapidly becoming the most sought-after resource in the technological world. We believe our IPU technology will become the worldwide standard for machine intelligence compute. The performance of Graphcore's processor, compared to other accelerators, is going to be transformative, whether you are a medical researcher, roboticist, online marketplace, social network or building autonomous vehicles. "At Graphcore, we are focused on building a successful, enduring company that can serve the needs of all of those communities, over the long term.