updates: FAQs, taglines (#7)

* Add files via upload: thumbnail for video as GH can't embed videos
* Provided updated faqs, project taglines

Author: jeanniefinks

docs/source/faqs.md

@@ -30,8 +30,7 @@ The DeepSparse Engine, created by Neural Magic, is a general purpose engine for
 
 Learn more about Neural Magic and the DeepSparse Engine (formerly known as the Neural Magic Inference Engine).
 
-<iframe width="560" height="315" src="https://www.youtube.com/embed/zJy_8uPZd0o" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen>
-</iframe>
+[![Watch the Why Neural Magic video](why-nm.jpg)](https://youtu.be/zJy_8uPZd0o)
 
 **How does Neural Magic make it work?**
 
@@ -53,7 +52,7 @@ We are open to opportunities to expand our support footprint for different CPU-b
 
 **Do you run on ARM architecture?**
 
-We currently do not support ARM and it’s on the Neural Magic roadmap; however, we’d still like to hear your use cases[Contact us to continue the conversation](https://neuralmagic.com/contact/).
+We currently do not support ARM and it’s on the Neural Magic roadmap; however, we’d still like to hear your use cases. [Contact us to continue the conversation](https://neuralmagic.com/contact/).
 
 **To what use cases is the Deep Sparse Platform best suited?**
 
@@ -63,6 +62,14 @@ We focus on the models and use cases related to computer vision due to cost sens
 
 Today, we offer support for CNN-based computer vision models, specifically classification and object detection model types. We are continuously adding models to [our supported model list and SparseZoo](https://docs.neuralmagic.com/sparsezoo). Additionally, we are investigating model architectures beyond computer vision such as NLP models like BERT.
 
+**Is dynamic shape supported?**
+
+Dynamic shape is currently not supported; be sure to use models with fixed inputs and compile the model for a particular batch size. Dynamic shape and dynamic batch sizes are on the Neural Magic roadmap; [subscribe for updates](https://neuralmagic.com/subscribe/).
+
+**Can multiple model inferences be executed?**
+
+Model inferences are executed as a single stream; concurrent execution is unsupported at this time.
+
 ___
 
 ## Benchmarking FAQs
@@ -73,7 +80,7 @@ This will vary but, in some cases, we are seeing 3x-10x savings. Typically, we o
 
 **Do you have benchmarks to compare and contrast?**
 
-Yes. Check out our [benchmark demo video](https://neuralmagic.com/blog/neural-magic-demo/) or [contact us to](https://neuralmagic.com/contact/) discuss your particular performance requirements. If you’d rather observe performance for yourself, [head over to the Neural Magic GitHub repo](https://github.com/neuralmagic) to check out our tools and generate your own benchmarks in your environment.
+Yes. Check out our [benchmark demo video](https://neuralmagic.com/blog/neural-magic-demo/) or [contact us](https://neuralmagic.com/contact/) to discuss your particular performance requirements. If you’d rather observe performance for yourself, [head over to the Neural Magic GitHub repo](https://github.com/neuralmagic) to check out our tools and generate your own benchmarks in your environment.
 
 **Do you publish ML Perf inference benchmarks?**
 
@@ -83,11 +90,15 @@ ___
 
 ## Infrastructure FAQs
 
+**Which instruction sets are supported and do we have to enable certain settings?**
+
+AVX-512, AVX2, and VNNI. The DeepSparse Engine will automatically utilize the most effective available instruction set for the task. Generally, if AVX-512 is available then we have no reason to use AVX2 instruction set. AVX-512 VNNI only comes into use for quantized models i.e., INT8 or UINT8.
+
 **Are you suitable for edge deployments (i.e., in-store devices, cameras)?**
 
 Yes, absolutely. We can run anywhere you have a CPU with x86 instructions, including on bare metal, in the cloud, on-prem, or at the edge. Additionally, our model optimization tools are able to reduce the footprint of models across all architectures. We only guarantee performance in the DeepSparse Engine.
 
-We’d love to hear from users highly interested in ML performance. If you want to chat about your use cases or how others are leveraging the Deep Sparse Platform, [please reach out](mailto: [email protected]). Or simply head over to the [Neural Magic GitHub repo](https://github.com/neuralmagic) and check out our tools.
+We’d love to hear from users highly interested in ML performance. If you want to chat about your use cases or how others are leveraging the Deep Sparse Platform, [please reach out]([email protected]). Or simply head over to the [Neural Magic GitHub repo](https://github.com/neuralmagic) and check out our tools.
 
 **Do you have available solutions or applications on the Microsoft/Azure platform?**
 

docs/source/getstarted.md

@@ -22,10 +22,10 @@ Neural Magic’s vision is no-hardware AI, shattering the hardware barriers that
 
 To deliver on this vision, there are several components to the Deep Sparse Platform:
 
-1. [Sparsify](https://docs.neuralmagic.com/sparsify): Open-source, easy-to-use interface to automatically sparsify and quantize deep learning models for CPUs & GPUs.
-2. [SparseML](https://docs.neuralmagic.com/sparseml): Open-source libraries and optimization algorithms for CPUs & GPUs, enabling integration with a few lines of code.
-3. [SparseZoo](https://docs.neuralmagic.com/sparsezoo): Open-source neural network model repository for highly sparse and sparse-quantized models with matching pruning recipes for CPUs and GPUs.
-4. [DeepSparse Engine](https://docs.neuralmagic.com/deepsparse):  Free CPU runtime that runs sparse models at GPU speeds.
+1. [Sparsify](https://docs.neuralmagic.com/sparsify): Easy-to-use UI for automatically sparsifying neural networks and creating sparsification recipes for better inference performance and a smaller footprint
+2. [SparseML](https://docs.neuralmagic.com/sparseml): Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models
+3. [SparseZoo](https://docs.neuralmagic.com/sparsezoo): Neural network model repository for highly sparse and sparse-quantized models with matching sparsification recipes
+4. [DeepSparse Engine](https://docs.neuralmagic.com/deepsparse): Neural network inference engine that delivers GPU-class performance for sparsified models on CPUs
 
 Our inference engine and model optimization technologies enable companies to use ubiquitous and unconstrained CPU resources to achieve performance breakthroughs, at scale, with all the flexibility of software.
 
@@ -62,11 +62,13 @@ Today, we offer support for convolutional neural network-based computer vision m
 
 We are continuously exploring models to add to our supported [model list](https://docs.neuralmagic.com/sparsezoo/models.html) and SparseZoo including model architectures beyond computer vision. Popular NLP models such as BERT are on the Neural Magic roadmap; [subscribe for updates](http://neuralmagic.com/subscribe).
 
-### PyTorch and ONNX
+### Notes
+
+#### PyTorch and ONNX
 
 Sparsify and the DeepSparse Engine inputs are standardized on the ONNX format. PyTorch has native ONNX export and requires fewer steps than other supported frameworks, such as [Keras or TensorFlow](https://docs.neuralmagic.com/sparseml/quicktour.html#exporting-to-onnx). If you have flexibility in frameworks, consider PyTorch to start.
 
-### Model Considerations
+#### Model Considerations
 
 Dynamic shape is currently not supported; be sure to use models with fixed inputs and compile the model for a particular batch size. Dynamic shape and dynamic batch sizes are on the Neural Magic roadmap; [subscribe for updates](http://neuralmagic.com/subscribe).