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+1 (503) 696-0831 Anyone can stuff a chip full of multipliers, doesn’t mean they can utilize them well. � �}y�|iW�v^1�H�Wrj(#�TI�׫&t����l�Gw:�ү*��ij���JM�1�LR�Y��*��i����*��n)X�ƕH3�IT�/��r�9��� L L. Workload: Task: Question Answering, Dataset: SQuAD, Base Model, Layers=12 , Hidden Size=768, Heads=12 , Intermediate Size=3,072, Max Seq Len = 128. Habana chief business officer Eitan Medina recently described his company’s architectural strategy at this year’s Hot Chips event. endobj Also, with Facebook working with several other accelerator chip start-ups, there is, of course, no guarantee that Habana will receive major orders from the social media giant. General details: Announced September 2018. endobj Total scenario power=100W Efficiency (images/sec/watt): 151 Latency: 1.3msec including drivers Our growing team of industry analysts and thought leaders should address all your needs. Trey Campbell stream endstream One particular element that is under user control is the accuracy loss that can be tolerated. NVIDIA’s GPUs have dominated the cloud data center AI training market for several years with many customers now regarding NVIDIA as having a vendor lock on them. The tested topology is BERTBase encoder, Layers=12 Hidden Size=768 Heads=12 Intermediate Size=3,072 Max Seq Len = 128. The GEMM operates on 16-bit integers. endobj At present, Habana supports TensorFlow for building models but plans to add support for PyTorch and other machine learning frameworks as well. On ResNet-50, Habana claims Gaudi systems can train up 3.8 times faster than a V100 GPU-based setup once you scale up to about 650 processors. Gareth has been a technology analyst for over 20 years and has compiled research reports and market share/forecast studies on a range of topics, including wireless technologies, AI & computing, automotive, smartphone hardware, sensors and semiconductors, digital broadcasting and satellite communications. 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. AI technologies are also developing at […], If we could sum up the near-term future of high performance computing in a single phrase, it would be more of the same and then […], As we’ve talked about in the past, the focus on data – how much is being generated, where it’s being created, the tools needed to […]. All the vendors in the space are designing their processors accordingly, at least the ones who are building separate products targeting each application area. Others, like Wave Computing, initially developed a dual-purpose architecture to address both areas, but switched strategies when they realized that the machine learning market was becoming more specialized. For example, Goya’s SRAM shared memory is managed by software, which the company says enables better use of the entire memory hierarchy. https://developer.nvidia.com/deep-learning-performance-training-inference. 4C�j1�ڜ��%�DzK>���(��4O�BӺU���+k�>q��ulL�Y�f}�S�#7�}��(��1G�g��T�K��Q~�h���9���G�'�7:]c��A�6�F�T �%�E�#��Na����I�T��n���g���V� 2��&��E��G�tQ��ƫپVQ��O���i�L唯�7 `���'l`�ê;�zzE!GM�%���(GWk���V]�o�ө_r5"� 3�+D�������ZX���滪�jB����ծ��v"D�Y��P����.י.n,2��)m�ً$H�,������[�h��m�`��|�Kҽ��ꖬ�I%�%�( Because of the long training times of neural networks – often days or weeks – throughput is critical. Each Gaudi processor has a whopping ten ports of 100 Gb/sec Ethernet that supports RDMA over Converged Ethernet (RoCE). Intel, the Intel logo, and other Intel marks are trademarks of Intel Corporation or its subsidiaries. endobj Artificial intelligence is a broad area, covering diverse fields such as image recognition, natural language processing (NLP), and robotics. This frees customers from NVIDIA’s proprietary software and interfaces. The model is converted into internal representation. In June, Israeli start-up Habana Labs announced Gaudi, a 16nm training chip for neural networks. It requires a manageable amount of bandwidth since they only need to exchange updates to the model as it’s trained. <> Please touch base for any queries or insights on tech industry. 6 0 obj Data parallelism works fine with standard network components, and as we said, is the conventional way to do training. Hardware: Goya HL-100 PCIe Card; CPU XEON E5 Habana released its inference processor, the Goya HL-1000, at the beginning of 2018. and lower total cost of ownership. Exhibit 2: Habana Labs HLS-1 System which combines eight Gaudi accelerator cards. AI data center & cloud performance, Not only do these two application areas present significantly different requirements for the hardware, but the markets for these systems also present their own particular needs. Shenoy continued: “We know that customers are looking for ease of programmability with purpose-built AI solutions, as well as superior, scalable performance on a wide variety of workloads and neural network topologies. It can process 15,000 ResNet-50 images/second with 1.3-ms latency at a batch size of 10 while running at 100 W (more than 5x the number of images than competing platforms). In cases like that, you move up to INT16 and take the performance hit. Aside from raw performance, an important characteristic of AI training processors is scalability. endobj x���S[e��� `]�T[�������8ꎶNW-�� It consequently has a smaller die size and lower power envelope. Get in Touch, @2020 copyrights Counterpoint Technology Market Research | All rights reserved. Inference processors need to provide reasonable amounts of mathematical performance (using a mix of lower precision floating point and integer), medium amounts of memory, and latency-hiding features – all at relatively low power. Each TPC has its own local memory, as well as access to shared memory in the form of SRAM. 2019 Habana Labs Ltd. | www.habana.ai | Ver 1.0 | June 2019 6 4. To understand the actual performance of any neural inference engine, you must know what batch size … But AI startup Habana Labs has been on this bifurcated path from its inception and the company’s two initial offerings do reflect these diverging requirements. 4 0 obj Habana chairman Avigdor Willenz has agreed to serve as a senior adviser to the business unit as well as to Intel. And since BERT is the basis for a lot of NLP models these days, the better performance should be realized in similar types of applications. And that mainly has to do with dichotomy between training and inference. For example, with the ResNet-50 model, the use of 8-bit integer (INT8) provides the best image recognition throughput, but with an accuracy loss of 0.4 percent for compared to a GPU baseline. “Intel has created a world-class AI team and capability. Habana is also introducing an 8 Gaudi chip system called HLS-1, which includes eight HL-205 Mezzanine cards, with PCIe connectors for external host connectivity and 24 100Gbps Ethernet ports for connecting to off-the-shelf Ethernet switches accommodated in a standard 19-inch rack (Exhibit 2).

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