No single processor architecture for server applications has ever been successful because no two workloads are the same. For the past decade, Intel had all but dominated the server market, effectively shutting out AMD, the only other x86 server vendor, and leaving only HPC, mainframe, database and other specialized applications for different processor architectures. With the industry facing challenges due to security vulnerabilities from defective speculative execution implementations, changing workload requirements, slowing of Moore’s Law, and innovative new processor architectures, server OEMs and customers are all looking at a rapidly evolving landscape and choices. Many of the new processor vendors are focusing on specific segments to target. IBM Power appears to be well positioned to benefit from the tremendous interest in AI.
In addition to the changing technical and market dynamics, Intel has also stumbled in both manufacturing process and architecture. They have struggled to transition to the 14nm and 10nm process nodes and now appear well behind Samsung and TSMC, the major leading foundry service providers, in transitioning to the 7nm process node. Also, Intel has purposely limited memory bandwidth in its Xeon processors to promote two-socket servers over single-socket configurations. This evolution in the industry has created a renewed opportunity for IBM with its Power architecture.
AI automates constant learning and discovery through data. However, AI is different from hardware-driven, robotic automation. Instead of automating manual tasks, AI performs frequent, high-volume, computerized tasks reliably and without fatigue. For this type of automation, a human inquiry is still essential to set up the system and ask the right questions.
AI adds intelligence to existing products. In most cases, AI will not be sold as an individual application. Instead, products you already use will be further enhanced with AI capabilities, much like Siri was added as a feature to a new generation of Apple products. Automation, conversational platforms, bots, and smart machines can be combined with large amounts of data to improve many technologies at home and in the workplace, from security intelligence to investment analysis.
AI adapts through progressive learning algorithms to let the data do the programming. AI finds structure and regularities in data so that the algorithm acquires a skill: The algorithm becomes a classifier or a predictor. So, just as the algorithm can teach itself how to play chess, it can teach itself what product to recommend next online. Moreover, the models adapt when given new data. Backpropagation is an AI technique that allows the model to adjust, through training and added data when the first answer is not quite right.
AI analyzes more and broader data using neural networks that have many hidden layers. Building a fraud detection system with five hidden layers was almost impossible a few years ago. All that has changed with incredible computing power and big data. You need lots of data to train deep learning models because they learn directly from the data. The more data you can feed them, the more accurate they become.
AI achieves incredible accuracy through deep neural networks – which was previously impossible. For example, your interactions with Alexa, Google Search, and Google Photos are all based on deep learning – and they keep getting more accurate the more we use them. In the medical field, AI techniques from deep learning, image classification, and object recognition can now be used to find cancer on MRIs with the same accuracy as highly trained radiologists.
AI gets the most out of data. When algorithms are self-learning, the data itself can become intellectual property. The answers are in the data; apply AI to get them out. Since the role of the information is now more critical than ever before, it can create a competitive advantage. If you have the best data in a competitive industry, even if everyone is applying similar techniques, the best data will win.
Mellanox AI Solutions accelerate many of the world’s leading artificial intelligence and machine learning platforms. Machine learning is a pillar of today’s technological world, offering solutions that enable better and more accurate decision making based on the enormous amounts of data being collected. Machine learning encompasses a wide range of applications, ranging from security, finance, and image and voice recognition, to self-driving cars, healthcare, and smart cities.
Mellanox adapters, switches, cables, and software implement the world’s fastest and most robust InfiniBand and Ethernet networking solutions for a complete, high-performance machine learning infrastructure. These capabilities ensure optimum application performance with:
Hardware is only half the story. In addition to the hardware improvements and network acceleration from Mellanox, IBM Power also developed tools expanding the capability of open source frameworks like TensorFlow and Caffe, as well as offering a machine-learning library called Snap ML that further reduces the training of new neural networks by leveraging generalized linear models rather than starting from scratch. Many of the libraries are being developed specifically for emerging applications like image and video recognition.
As workloads change, server customers are looking for more options to meet an increasing array of performance requirements by the growing number of diverse workloads. In the case of AI, IBM has developed a niche in combination with its partners NVIDIA for GPU’s and Mellanox for the world’s leading interconnect solutions that provides a platform that offers the highest performance for AI, along with IBM Power.