Preview. Read more…, Ayar Labs Brings National Lab Architects And Industry Leaders Together To Talk Disaggregation, On The Edge With NVM-Express And Object Storage. 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. Small. the first AI processor to implement Habana claims it has already shipped Goya to 20 select clients. �Z腂�v)e�v��! Habana withheld most of the information regarding the GEMM engine. :�^��*+��j]o�����6�{�N�:vZ������ǻ÷G ��o Featuring highlights, analysis, and stories from the week directly from us to your inbox with nothing in between. Other names and brands may be claimed as the property of others. Purpose-Built for AI Training. endobj Habana’s AI Training and Inference Products. endobj and lower total cost of ownership. 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. With its freedom from proprietary software and interfaces – and probably a much lower price – it should appeal to cloud data center customers who currently buy expensive NVIDIA GPUs and are anxious to see alternative suppliers. The reason behind this, says Medina, is that to support faster model training, you have to be able to scale the hardware effectively both within the node and across multiple nodes. Habana will report to Intel’s Data Platforms Group, home to Intel’s broad portfolio of data center class AI technologies. Additionally, Habana’s Goya AI Inference Processor, which is commercially available, has demonstrated excellent inference performance including throughput and real-time latency in a highly competitive power envelope. The Gaudi training chip is currently sampling with unnamed hyperscale customers. �}�@�� g�ࣕ9!� ��c��.�_]0[���ʜ©�S��V The Goya processor delivers 1.67x to 2.06x (batch 12/24 respectively) higher throughput than the T4 on the SQuAD task, all at significantly lower latency. Following this step, ahead-of-time (AOT) compilation is used to optimize the model and create a working plan for the network model execution on the Goya hardware. Nevertheless, if its technology delivers as promised, Intel-backed Habana could emerge as one of the leading challengers to NVIDIA in the AI training market. Habana claims it has already shipped Goya to 20 select clients.

Habana’s training chip, known as the Gaudi HL-2000, shares a number of design elements with Goya, but overall is a much different architecture. Single Die Cost ~ $1000/670 ~ $1.5. Die Area ~ 10 mm x 10 mm ~ 100 mm² ( TPU V1 Die Size ~ 331 mm², SRAM Cell in 32 nm area ~ 0.18 um²) Dies per wafer ~ 70,650 / 100 ~ 706 (Actually less due to edge defects etc) Actually good dies with 95% yield ~ 0.95 * 706 ~ 670. Since each chip provide three ports for external communication, that comes to 24 ports per HLS-1. %���� © 2020 Habana Labs Ltd. All rights reserved. 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). Your email address will not be published.

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.

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. A high-level Gaudi for training and Goya for inference offer a rich, easy-to-program development environment to help customers deploy and differentiate their solutions as AI workloads continue to evolve with growing demands on compute, memory and connectivity.

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