Compare Intel Movidius Myriad X VPU vs. Intel Nervana Neural Network Processor

Intel Movidius Myriad X VPU is ranked 6th in Enterprise GPU while Intel Nervana Neural Network Processor is ranked 9th in Enterprise GPU. Intel Movidius Myriad X VPU is rated 0, while Intel Nervana Neural Network Processor is rated 0. On the other hand, Intel Movidius Myriad X VPU is most compared with Intel Movidius Myriad 2 VPU, whereas Intel Nervana Neural Network Processor is most compared with .
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Ranking
6th
out of 11 in Enterprise GPU
Views
61
Comparisons
37
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0
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0
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9th
out of 11 in Enterprise GPU
Views
5
Comparisons
4
Reviews
0
Average Words per Review
0
Avg. Rating
N/A
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Also Known As
Movidius Myriad X, Myriad X, Myriad X VPUIntel Neural Network Processors, Intel NNP
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Overview

The Intel® Movidius™ Myriad™ X VPU is the third generation and most advanced VPU from Intel. It is the first of its class to feature the Neural Compute Engine – a dedicated hardware accelerator for deep neural network inferences. The Neural Compute Engine in conjunction with the 16 powerful SHAVE cores and an ultra-high throughput intelligent memory fabric makes Intel Movidius Myriad X the industry leader for on-device deep neural networks and computer vision applications. Intel’s Myriad™ X VPU has received additional upgrades to imaging and vision engines including additional programmable SHAVE cores, upgraded and expanded vision accelerators, and a new native 4K ISP pipeline with support for up to 8 HD sensors connecting directly to the VPU.

The Intel Movidius Myriad X VPU is programmable via the Myriad Development Kit (MDK) which includes all necessary development tools, frameworks and APIs to implement custom vision, imaging and deep neural network workloads on the chip.

Key Benefits
-Dedicated Neural Compute Engine
-16 High Performance SHAVE Cores
-Enhanced ISP with 4K support
-New Vision Accelerators including Stereo Depth

The Intel Nervana Neural Network Processor (NNP) is a purpose built architecture for deep learning. This new architecture provides the needed flexibility to support all deep learning primitives while making core hardware components as efficient as possible.

Deep learning is taking the next leap forward. Increasingly sophisticated and complex data, models, and techniques will allow AI to move beyond identifying information to understanding context – enabling a level of “common sense” for reasoning and decision-making.

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