Intel just introduced their Nervana AI accelerators – the Nervana NNP-T1000 for training, and Nervana NNP-I1000 for inference!
Here is EVERYTHING you need to know about these two new Intel Nervana AI accelerators!
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.
Nervana NNP-T For Training
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.
Recommended : Intel NNP-T1000 PCIe + Mezzanine Cards Revealed!
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-T Accelerator Models
The Intel Nervana NNP-T 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|
Nervana NNP-I For Inference
The Intel Nervana NNP-I1000, on the other hand, is optimised for multi-modal inferencing of near-real-time, high-volume compute.
Each Nervana NNP-I1000 features 12 Inference Compute Engines (ICE), which are paired with two Intel CPU cores, a large on-die 75 MB SRAM cache and an on-die Network-on-Chip (NoC).
Recommended : Intel NNP-I1000 PCIe + M.2 Cards Revealed!
It offers mixed-precision support, with a special focus on low-precision applications for near-real-time performance.
Like the NNP-T, the NNP-I comes with a full software stack that is built with open components, including direct integration with deep learning frameworks.
Intel Nervana NNP-I Accelerator Models
The NNP-I1000 comes in a 12 W M.2 form factor, or a 75 W PCI Express card, to accommodate exponentially larger and more complex models, or to run dozens of models and networks in parallel.
|Specifications||Intel Nervana NNP-I1100||Intel Nervana NNP-I1300|
|Form Factor||M.2 Card||PCI Express Card|
|Compute||1 x Intel Nervana NNP-I1000||2 x Intel Nervana NNP-I1000|
|SRAM||75 MB||2 x 75 MB|
|Int8 Performance||Up to 50 TOPS||Up to 170 TOPS|
|TDP||12 W||75 W|
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