The new Intel Nervana NNP-T1000 neural network processor comes in PCIe and Mezzanine card options designed for AI training acceleration.
Here is EVERYTHING you need to know about the Intel Nervana NNP-T1000 PCIe and Mezzanine card options!
Intel Nervana Neural Network Processors
Intel Nervana neural network processors, NNPs for short, are designed to accelerated two key deep learning technologies – training and inference.
To target these two different tasks, Intel created two AI accelerator families – Nervana NNP-T that’s optimised for training, and Nervana NNP-I that’s optimised for inference.
They are both paired with a full software stack, developed with open components and deep learning framework integration.
Intel Nervana NNP-T1000
The Intel Nervana NNP-T1000 is not only capable of training even the most complex deep learning models, it is highly scalable – offering near linear scaling and efficiency.
By combining compute, memory and networking capabilities in a single ASIC, it allows for maximum efficiency with flexible and simple scaling.
Each Nervana NNP-T1000 is powered by up to 24 Tensor Processing Clusters (TPCs), and comes with 16 bi-directional Inter-Chip Links (ICL).
Its TPC supports 32-bit floating point (FP32) and brain floating point (bfloat16) formats, allowing for multiple deep learning primitives with maximum processing efficiency.
Its high-speed ICL communication fabric allows for near-linear scaling, directly connecting multiple NNP-T cards within servers, between servers and even inside and across racks.
- High compute utilisation using Tensor Processing Clusters (TPC) with bfloat16 numeric format
- Both on-die SRAM and on-package High-Bandwidth Memory (HBM) keep data local, reducing movement
- Its Inter-Chip Links (ICL) glueless fabric architecture and fully-programmable router achieves near-linear scaling across multiple cards, systems and PODs
- Available in PCIe and OCP Open Accelerator Module (OAM) form factors
- Offers a programmable Tensor-based instruction set architecture (ISA)
- Supports common open-source deep learning frameworks like TensorFlow, PaddlePaddle and PyTorch
Intel Nervana NNP-T1000 Models
The Intel Nervana NNP-T1000 is currently available in two form factors – a dual-slot PCI Express card, and a OAM Mezzanine Card, with these specifications :
|Specifications||Intel Nervana NNP-T1300||Intel Nervana NNP-T1400|
|Form Factor||Dual-slot PCIe Card||OAM Mezzanine Card|
|Compliance||PCIe CEM||OAM 1.0|
|Compute Cores||22 TPCs||24 TPCs|
|Frequency||950 MHz||1100 MHz|
|SRAM||55 MB on-chip, with ECC||60 MB on-chip, with ECC|
|Memory||32 GB HBM2, with ECC||32 GB HBM2, with ECC|
|Memory Bandwidth||2.4 Gbps (300 MB/s)|
|Inter-Chip Link (ICL)||16 x 112 Gbps (448 GB/s)|
|ICL Topology||Ring||Ring, Hybrid Cube Mesh,
|I/O to Host CPU||PCIe Gen3 / Gen4 x16|
|Thermal Solution||Passive, Integrated||Passive Cooling|
|TDP||300 W||375 W|
|Dimensions||265.32 mm x 111.15 mm||165 mm x 102 mm|
Intel Nervana NNP-T1000 PCIe Card
This is what the Intel Nervana NNP-T1000 (also known as the NNP-T1300) PCIe card looks like :
Intel Nervana NNP-T1000 OAM Mezzanine Card
This is what the Intel Nervana NNP-T1000 (also known as NNP-T1400) Mezzanine card looks like :
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