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Machine Learning Transforms Cloud Providers into Custom Chip Developers
Machine learning is a subfield of artificial intelligence (AI) and computer science that focuses on using data and algorithms to mimic human learning processes and progressively increase accuracy.
The developing discipline of data science heavily relies on machine learning. In data mining projects, algorithms are taught to generate classifications or predictions using statistical approaches, revealing important insights. These insights then influence decision-making within applications and enterprises, perhaps impacting significant growth KPIs. Big data's continued growth and expansion will drive up demand for data scientists, who will be needed to help identify the most important business issues and the data needed to answer them properly.
Google TensorFlow
Google has a cloud platform called Google Cloud Platform (GCP). Google provides a group of open cloud computing services under the name Google Cloud. The platform comes with several hosted services for computing, storing, and creating applications that are powered by Google hardware. Software developers, cloud administrators, and other company IT experts can use a dedicated network connection or the open internet to access Google Cloud services.
A complete open-source platform for machine learning is called TensorFlow. Although TensorFlow is a comprehensive framework for controlling all facets of a machine learning system, this workshop focuses on utilizing a specific TensorFlow API to create and train machine learning models.
To speed up its machine learning algorithms, Google created its special processors. Tensor Processing Units (TPUs) are the processors the firm initially unveiled at its I/O developer conference back in May 2016. However, the company didn't provide much information about them beyond stating that they were designed for TensorFlow, its machine-learning framework.
In comparison to a typical GPU/CPU combo, the TPUs execute Google's routine machine learning workloads 15x to 30x quicker on average (in this case, Intel Haswell processors and Nvidia K80 GPUs). Additionally, the TPUs deliver 30x to 80x greater TeraOps/Watt since data centers pay attention to power usage (with faster memory in the future, those numbers will probably increase).
Microsoft Brainwave
For real-time AI inference in the cloud and on edge, Project Brainwave is a deep learning platform. Inferencing from deep neural networks (DNNs) is sped up by a soft neural processing unit (NPU), which is based on a high-performance field-programmable gate array (FPGA) and has uses in computer vision and natural language processing. By adding a linked, programmable compute layer made of silicon to CPUs, Project Brainwave is revolutionizing computing.
Microsoft data centers can offer pre-trained DNN models with high efficiency and small batch sizes using a high-performance, precision-adaptable FPGA soft processor. Future-proofing the infrastructure is the usage of an FPGA, which makes it adaptable for ongoing advancements and upgrades.
A single DNN model may be implemented as a scalable hardware microservice that uses several FPGAs to construct web-scale services by using FPGAs on a datacenter-scale computing fabric. This can process enormous volumes of data instantly.
Cloud operators are resorting to specialized hardware for increased efficiency and performance, particularly for live data streams, to satisfy the expanding computing needs of deep learning. Low latency, high throughput, and high efficiency are all features of Project Brainwave, along with the adaptability of field-programmability, making it the trifecta of high-performance computing.
It can keep up with new developments and meet the demands of quickly evolving AI algorithms since it is built on an FPGA.
Amazon Inferentia
The goal of AWS is to democratize access to cutting-edge technology by making deep learning ubiquitous for regular developers and making it affordable to use on a pay-as-you-go basis. The first bespoke silicon created by Amazon to speed up deep learning workloads is called AWS Inferentia, and it is a key component of a long-term plan to carry out this objective. AWS Inferentia is intended to offer high-performance inference in the cloud, lower the overall cost of inference, and make it simple for developers to incorporate machine learning into their commercial applications.
In order to use the Inferentia chip, the user needs first set up an AWS Neuron software development kit (SDK) instance and invoke the chip through it. To provide customers with the best Inferentia experience possible, Neuron has been incorporated into the AWS Deep Learning AMI (DLAMI).
Conclusion
Depending on the size and complexity of the chip, manufacturing any chip (ASIC, SOC, etc.) is a time-consuming, expensive operation that often employs teams of 10 to 1000 individuals. Machine learning is increasingly being used in fields including fashion recommendation, railroad maintenance, and even medical diagnosis.
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