It's our first complete system-on-chip designed to run and train deep learning models faster and more efficiently than a general-purpose CPU. A decade ago, modern AI was born. A team of academic researchers showed that with millions of photos and days of brute force computation, a deep learning model could be trained to identify objects and animals in entirely new images. Today, deep learning has evolved from classifying pictures of cats and dogs to translating languages, detecting tumors in medical scans, and performing thousands of other time-saving tasks. AI models are growing exponentially, but the hardware to train these behemoths and run them on servers in the cloud or on edge devices like smartphones and sensors hasn't advanced as quickly.

A decade ago, modern AI was born. A team of academic researchers showed that with millions of photos and days of brute force computation, a deep learning model could be trained to identify objects and animals in entirely new images. Today, deep learning has evolved from classifying pictures of cats and dogs to translating languages, detecting tumors in medical scans, and performing thousands of other time-saving tasks. AI models are growing exponentially, but the hardware to train these behemoths and run them on servers in the cloud or on edge devices like smartphones and sensors hasn't advanced as quickly. That's why the IBM Research AI Hardware Center decided to create a specialized computer chip for AI.

At the annual Hot Chips conference, IBM (NYSE: IBM) today unveiled details of the upcoming new IBM Telum Processor, designed to bring deep learning inference to enterprise workloads to help address fraud in real-time. Telum is IBM's first processor that contains on-chip acceleration for AI inferencing while a transaction is taking place. Three years in development, the breakthrough of this new on-chip hardware acceleration is designed to help customers achieve business insights at scale across banking, finance, trading, insurance applications and customer interactions. A Telum-based system is planned for the first half of 2022. According to recent Morning Consult research commissioned by IBM, 90% of respondents said that being able to build and run AI projects wherever their data resides is important1.

At the annual Hot Chips conference, IBM (NYSE: IBM) today unveiled details of the upcoming new IBM Telum Processor, designed to bring deep learning inference to enterprise workloads to help address fraud in real-time. Telum is IBM's first processor that contains on-chip acceleration for AI inferencing while a transaction is taking place. Three years in development, the breakthrough of this new on-chip hardware acceleration is designed to help customers achieve business insights at scale across banking, finance, trading, insurance applications and customer interactions. A Telum-based system is planned for the first half of 2022. According to recent Morning Consult research commissioned by IBM, 90% of respondents said that being able to build and run AI projects wherever their data resides is important1.

IBM's new AI Hardware Composer tool aims to boost the performance of analog AI hardware. The tool is being released on the second anniversary of the IBM Research AI Hardware Center. IBM's pioneering centre launched in 2019 with the aim of improving AI hardware compute efficiency by 2.5 times every year for a decade. AI Hardware Composer claims to help both novice and experienced developers to create neural networks and tune analog devices to build accurate AI models. The new tool can be used with IBM's existing Analog Hardware Acceleration Kit (AIHWKIT), an open-source Python toolkit for exploring and using the capabilities of in-memory computing devices in the context of artificial intelligence.

Designed to push the frontiers of computing chip and systems performance optimized for AI workloads, an 8 petaflop IBM Power9-based supercomputer has been unveiled in upstate New York that will be used by IBM data and computer scientists, by academic researchers and by industrial and commercial end-users. Installed at the New York State-IBM Research AI Hardware Center in Albany, NY, the system -- called AiMOS (Artificial Intelligence Multiprocessing Optimized System) – was the most powerful to debut on last month's Top500 supercomputer ranking, it's listed as the world's 24th most powerful computer, the most powerful to be housed at a private university and – according to the Green500 listing – the third most energy efficient. It was built using the same IBM Power Systems technology as the Top500's nos. 1 and 2, systems, the US Dept. of Energy's IBM Summit and Sierra supercomputers, based on IBM Power9 CPUs and Nvidia GPUs. AiMOS is the result of a collaboration between IBM, RPI and two New York state programs, Empire State Development (ESD) and NY CREATES. Named for Rensselaer co-founder Amos Eaton, AiMOS will serve as a test bed for computation, modeling and simulation of hardware "designed to push the boundaries of AI performance," IBM said.

Part of a collaboration between IBM, Empire State Development (ESD), and NY CREATES, the eight petaflop IBM POWER9-equipped AI supercomputer is configured to help enable users to explore new AI applications and accelerate economic development from New York's smallest startups to its largest enterprises. Named AiMOS (short for Artificial Intelligence Multiprocessing Optimized System in honor of Rensselaer co-founder Amos Eaton, the machine will serve as a test bed for the New York State - IBM Research AI Hardware Center, which opened on the SUNY Polytechnic Institute (SUNY Poly) campus in Albany earlier this year. The AI Hardware Center aims to advance the development of computing chips and systems that are designed and optimized for AI workloads to push the boundaries of AI performance. AiMOS will provide the modeling, simulation, and computation necessary to support the development of this hardware. "Computer artificial intelligence, or more appropriately, human augmented intelligence (AI), will help solve pressing problems, from healthcare to security to climate change. In order to realize AI's full potential, special purpose computing hardware is emerging as the next big opportunity," said Dr. John E. Kelly III, IBM Executive Vice President.

Sophisticated machine learning applications require not only enormous amounts of training data, but powerful computer hardware on which to train. An analysis conducted by San Francisco research firm OpenAI found that since 2012, the amount of compute used in the largest training runs has been increasing exponentially with a 3.4-month doubling time, and that it's grown by more than 300,000 times over that same time period. The trend spurred the development of supercomputers like the U.S. Department of Energy's Sierra and Summit, which leverage dedicated accelerator chips to speed up AI computation. Now, IBM's Hardware Center, in collaboration with New York State, SUNY Polytechnic Institute, and other members of IBM's AI Hardware Center, has delivered a new machine for the Department of Computer Science at Rensselaer Polytechnic Institute (RPI) that's optimized for state-of-the-art machine learning workloads. It's dubbed Artificial Intelligence Multiprocessing Optimized System, or AiMOS (in honor of Rensselaer cofounder Amos Eaton), and it will principally tackle projects in biology, chemistry, the humanities, and related domains underway at the new IBM Research AI Hardware Center on the SUNY campus in Albany.

IBM wants to develop next-generation artificial intelligence chips, and it's building a new AI research center and partnering with academia and other tech companies to do it. At the recently announced future AI Hardware Center at SUNY Polytechnical Institute in Albany, New York, IBM researchers will collaborate with academic researchers and tech partners to develop, prototype, and test new AI chips and systems. Initial partners include Samsung, Mellanox Technologies, Synopsis, Applied Materials, and Tokyo Electron. Related: Intel Steps Up Its Challenge to Nvidia's AI Chip Dominance, with Facebook's Help The IBM Research division, which has designed several prototypes of its Digital AI cores and Analog AI cores in recent years, will continue to develop these chips at the center, Jeff Burns, IBM Research's director of AI Compute and director of the future AI Hardware Center, said. These new processors are expected to result in a 1,000-times improvement in AI compute performance efficiency over the next 10 years.

Gov. Andrew Cuomo recently revealed plans by IBM to invest $2 billion in its new artificial intelligence (AI) research hub that will be located at the Albany, New York-based SUNY Polytechnic Institute. The new artificial intelligence research hub or AI Hardware Center is expected to concentrate on computer chip research, creation, prototyping, carrying out tests and simulation. Governor Andrew Cuomo claimed that the new center would help in attracting new entities in the federal research and artificial intelligence field as well as create numerous jobs. "Artificial intelligence has the potential to transform how we live and how businesses operate, and this partnership with IBM will help ensure New York continues to be on the cutting edge developing innovative technologies," Cuomo said in a statement. IBM also intends to extend and expand its collaboration with SUNY's Center for Semiconductor Research by a minimum of two years and possibly through 2028.