Transformer models have achieved remarkable success in various machine learning tasks but suffer from high computational complexity and resource requirements. The quadratic complexity of the self-attention mechanism further exacerbates these challenges when dealing with long sequences and large datasets. Specialized AI hardware accelerators, such as the Habana GAUDI architecture, offer a promising solution to tackle these issues. GAUDI features a Matrix Multiplication Engine (MME) and a cluster of fully programmable Tensor Processing Cores (TPC). This paper explores the untapped potential of using GAUDI processors to accelerate Transformer-based models, addressing key challenges in the process. Firstly, we provide a comprehensive performance comparison between the MME and TPC components, illuminating their relative strengths and weaknesses. Secondly, we explore strategies to optimize MME and TPC utilization, offering practical insights to enhance computational efficiency. Thirdly, we evaluate the performance of Transformers on GAUDI, particularly in handling long sequences and uncovering performance bottlenecks. Lastly, we evaluate the end-to-end performance of two Transformer-based large language models (LLM) on GAUDI. The contributions of this work encompass practical insights for practitioners and researchers alike. We delve into GAUDI's capabilities for Transformers through systematic profiling, analysis, and optimization exploration. Our study bridges a research gap and offers a roadmap for optimizing Transformer-based model training on the GAUDI architecture.

As the use of machine learning grows and gains complexity, the cost and time of training models can be challenging for enterprises. Amazon Web Services Inc. announced that its EC2 will be powered by Gaudi accelerators from Habana Labs, an Intel company, to increase the efficiency of this process, according to Chetan Kapoor (pictured, left), senior manager of product management, EC2, at AWS. "As we all know, training of deep learning models is a really computationally extensive task; oftentimes, it takes too long and costs too much," he said. "We expect for [these instances] to provide up to 40% better price performance than top-of-the-line GPU instances." Kapoor and Eitan Medina (pictured, right), chief business officer at Habana Labs Ltd., spoke with John Furrier, host of theCUBE, SiliconANGLE Media's livestreaming studio, during AWS re:Invent. They discussed the main characteristics of the partnership between AWS and Habana, the benefits of the solution for enterprises, and important use cases.

Agricultural production has become a complex business that requires the accumulation and integration of knowledge, in addition to information from many different sources. To remain competitive, the modern farmer often relies on agricultural specialists and advisors who provide them with information for decision making in their crops. But unfortunately, the help of the agricultural specialist is not always available when the farmer needs it. To alleviate this problem, expert systems have become a powerful instrument that has great potential within agriculture. This paper presents an Expert System for the diagnosis of diseases and pests in rice, tobacco, tomato, pepper, corn, cucumber and bean crops. For the development of this Expert System, SWI-Prolog was used to create the knowledge base, so it works with predicates and allows the system to be based on production rules. This system allows a fast and reliable diagnosis of pests and diseases that affect these crops.

Last month, Intel announced that it would acquire Israeli AI chip startup Habana Labs for $2B. At the time, I opined that this probably spelled the end for chips from the 2016 Nervana acquisition. Intel planned to bring out both the inference and the training versions of Nervana's second attempt to out-perform NVIDIA by the end of 2019. Apparently, something ugly happened on the way to the data center. Now, as expected, Intel has announced that Nervana will be no more.

In a tweet on Friday, deep learning analyst Karl Freund announced that Intel would "close the door" on Nervana, the deep learning chip startup Intel acquired in 2016, and instead focus on Habana Labs, the other startup that Intel acquired in December for almost $2 billion. Intel informed Freund of its new AI strategy going forward. Intel will support the NNP-I inference chip "for previously committed customers," but says that it will completely cease development of the NNP-T AI training design. Intel stopping development of the NNP-T doesn't come as a complete surprise, given the acquisition of Habana in December: both companies make chips targeted at artificial intelligence workloads in the data center (deep neural networks). At the time of the acquisition, there was already much speculation about what this implied for Nervana.

FILE - In this Oct. 1, 2019, file photo the symbol for Intel appears on a screen at the Nasdaq ... [ ] MarketSite, in New York. Intel said Monday, Dec. 16, that it has bought Israeli artificial intelligence startup Habana Labs for $2 billion. This week Intel agreed to pay roughly $2 billion for Habana Labs. Based in Israel and founded in 2015, the company is a startup focused on AI (Artificial Intelligence) chips. Keep in mind that Habana has raised a total of $75 million, which is a fairly modest amount for a hardware company (Intel Capital was one of the investors). According Intel's executive vice president and general manager of the Data Platforms Group, Navin Shenoy: "This acquisition advances our AI strategy, which is to provide customers with solutions to fit every performance need–from the intelligent edge to the data center.

Intel has struck a US$2 billion deal for Israel-based AI firm Habana Labs, a developer of programmable deep learning accelerators for the data center. The acquisition will "turbocharge" Intel's offerings for the data center, it said in a release, "with a high-performance training processor family and a standards-based programming environment to address evolving AI workloads." Navin Shenoy, Executive Vice President and General Manager of the Data Platforms Group at Intel, said; "This acquisition advances our AI strategy, which is to provide customers with solutions to fit every performance need – from the intelligent edge to the data center." Habana will remain an independent business unit and will continue to be led by its current management team, reporting to Intel's Data Platforms Group, home to Intel's broad portfolio of data center-class AI technologies. Intel said it expects the AI chip market to exceed US$25 billion by 2024.

Intel Corporation has acquired the Habana Labs, for around $2 billion. The acquisition strengthens the chip marker AI portfolio and accelerates its efforts in the AI silicon market. Intel estimates the total addressable market (TAM) for AI silicon to be greater than $25 billion by 2024. The AI chip market in the data center to be greater than $10 Billion, notes Intel in the announcement. Moreover, the two companies can accelerate the delivery of AI products for the data center, addressing customer evolving needs.

Intel Corporation this week announced that it has acquired Habana Labs for approximately $2 billion. Habana is an Israel-based company that develops programmable deep learning accelerators for the data centre. This acquisition is aimed at strengthening Intel's artificial intelligence portfolio and accelerate its efforts in the AI silicon market, which Intel expects to be greater than $25 billion by 2024. "This acquisition advances our AI strategy, which is to provide customers with solutions to fit every performance need – from the intelligent edge to the data centre," said Navin Shenoy, executive VP at Intel, in a press release. In July, Habana announced its Gaudi AI training processor, which the Tel Aviv startup promised was capable of beating GPU-based systems by 4x.

SANTA CLARA Calif., Dec. 16, 2019 – Intel Corporation today announced that it has acquired Habana Labs, an Israel-based developer of programmable deep learning accelerators for the data center for approximately $2 billion. The combination strengthens Intel's artificial intelligence (AI) portfolio and accelerates its efforts in the nascent, fast-growing AI silicon market, which Intel expects to be greater than $25 billion by 20241. "This acquisition advances our AI strategy, which is to provide customers with solutions to fit every performance need – from the intelligent edge to the data center," said Navin Shenoy, executive vice president and general manager of the Data Platforms Group at Intel. "More specifically, Habana turbo-charges our AI offerings for the data center with a high-performance training processor family and a standards-based programming environment to address evolving AI workloads." Intel's AI strategy is grounded in the belief that harnessing the power of AI to improve business outcomes requires a broad mix of technology – hardware and software – and full ecosystem support.