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Over the past few decades, almost unimaginable progress has been seen in computing performances and performance, which has been enabled by Moore’s law and has been assisted by Scale Out commodities hardware and loose software. This architecture has provided billions online globally and has practically put all human knowledge on our finger.
But the next computing revolution will make a lot of demand. To fulfill the promise of the AI, a step change is needed more than the development of the Internet era. To achieve this goal, we have to revise the industry as a basis that has advanced the previous change and collectively innovated to revise the entire technology stack. Let’s look for the pursuit of this stir and look like this architecture.
From Ajas Hardware to Special Compute
For decades, the dominant trend of computing has been the democratic democracy through scale out architecture that has been built on commodity servers almost the same. This uniformity is allowed to replace flexible workload and use effective resources. General AI’s demands relies heavily on mathematical operations predicting large -scale datases, changing this trend.
We are now witnessing a decisive change towards special hardware-including ASICS, GPUS, and Tensor Processing Unit (TPUs)-which provides orders to improve intensity of dollars and per watts per dollar and per watts compared to general purpose CPUs. This spread of specific computing units related to domain, which has been improved for tight tasks, will be important to advance the rapid growth in AI.
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Beyond Ethernet: The height of special bilateral contacts
These special systems often require “all -to -all” communication, with Terbet per second with bandout and nano -second latanese that approach local memory speed. Today’s networks, large -scale commodities are based on Ethernet switches and TCP/IP protocols, are invalid to handle these extreme demands.
As a result, we are looking at the rise of special bilateral contacts in the wide clusters of special accelerators, such as ICI and NV link for TPU. This purpose -constructed networks prefer memory transfer from direct memory and use dedicated hardware to accelerate the share of information among processors, effectively ignoring the traditional, layered networking stacks overdose.
This move will be necessary to overcome communication barriers and to effectively scale the next generation of AI, this move towards computing centenary networking.
Break the wall of memory
For decades, the benefits of calculation have pushed the growth in memory bandout. Although techniques like catches and stacked SRAM have partially reduced it, the nature of AI data is only increasing this problem.
Rapidly caused high bandwidth memory (HBM) due to an unpleasant need for feeding powerful computing units, which directs directly on the processor package to promote bandout and reduce delays. However, even HBM also faces basic limits: the physical chip circle limits the total data flu, and transmits large -scale datases at the speed of the terrace.
The significant requirement of high bandwidth connectivity has been highlighted in these limits and has been identified for the progress of processing and memory architecture. Without these innovations, our powerful computer resources will sit in vain waiting for data, which will dramatically limit the performance and scale.
From server forms to high density system
Today’s modern machine learning (ML) models often carefully rely on archetyped calculations that use hundreds of thousands of computing elements in tens of tens. At the micro -second level, these tough couples and the harmony of excellent grains are in harmony. Unlike the system that accepts conflicting, ML computation requires uniform elements. Mixing generations will hinder sharp units. Communication routes should also be planned and very effective, as delaying the same factor can prevent a whole process.
These highly demands of harmony and power are pushing the need for unprecedented computer density. Minimizing physical distance between processors is necessary to delay and reduce power consumption, paving the way for a new class of ultra -dense AI system.
This drive of extremely density and firmly integrated computation changes the maximum design for infrastructure, which is demanded to re -consider physical setting and dynamic power management to prevent performance barriers and maximize efficiency.
A new point for error tolerance
Traditional error tolerance relys on spare in the loose systems to achieve high -up -time. ML computing demands a different approach.
First of all, the scale of the calculation is more expensive. Second, the model training is a firmly synchronized process, where a single failure can clash with thousands of processors. Finally, advanced ML hardware often pushes the current technology limits, which potentially increases the rate of failure.
Instead, the emerging strategy includes repeated checkpointing-child computation state-in combination with real-time monitoring, rapid allocation of spare resources and immediate resume. Basic hardware and network design should be able to detect rapid failure to maintain efficiency and change the change of smooth ingredients.
More sustainable approach to strength
Today and waiting, access to power is an important obstacle to scaling AI computes. Although the maximum performance in the traditional system design is focused on maximum performance, we must go from the end to the end design, which focuses on the Deliverd, AT scale performance per watt. This approach is very important because it considers all the components of the system – compute, network, memory, power supply, cooling and fault tolerance – working together to maintain performance. Improving ingredients in isolation strictly limit the system’s overall performance.
As we emphasize maximum performance, individual chips require more strength, which often exceeds the cooling capacity of traditional air -cooled data centers. This requires a change toward more energy, but eventually more efficient, liquid cooling solution, and the fundamental new design of data center cooling infrastructure.
Beyond cooling, traditional useful power sources, such as dual utility feeds and diesel generators, produce slow delivery of financial costs and capacity. Instead, we have to collect diverse power sources and storage on a multi -gigate scale, which is administered by real -time micrograde controllers. By taking advantage of AI workload flexibility and geographical distribution, we can provide more potential without an expensive backup system that requires only a few hours every year.
This developing power model enables real-time reaction to the availability of electricity-from shutting down computers during the constituency to modern techniques, such as frequency scaling for workloads, can withdraw less performance. Real -time telemetry and process are needed at the levels not currently available for all of them.
Security and Privacy: Baked, Bolt not
One of the important lessons of the Internet era is that security and privacy cannot be effectively bolt on any existing architecture. The risks by bad actors will only develop and more sophisticated, in which consumer data and proprietary intellectual properties need to be constructed in the fabric of ML infrastructure. An important observation is that AI, in the end, will increase the capabilities of the invaders. As a result, this means that we have to make sure that the AI spare our defense simultaneously.
These include the end of data encryption, tracking of strong data lineage with certified accessories, to protect sensitive computations and sophisticated key management systems. Connecting these protective measures will be necessary to protect consumers and maintain their confidence. The real -time surveillance, which will be the second of the pitcher/telemetry and logging, will have the key to identifying and neutralizing high -stack attack vector in the needle, including those from internal risks.
Speed as a strategic mandatory
Hardware upgrade rhythm has changed dramatic. Unlike traditional infrastructure’s additional rack by -rack evolution, deployment of ML supercomputers requires a basically different approach. The reason for this is that ML computers do not easily run on contradictory deployments. Compute codes, algorithms and composers should fully take advantage of their capabilities, especially with each new hardware breed. Innovation rates are also unique, which often provides two or more elements of new hardware throughout the year.
Therefore, instead of additional upgrades, a mass of uniform hardware and simultaneous rollout, which is often needed throughout the data centers. With the annual hardware provides the improvement of the numerical factor performance, the ability to rapidly eradicate these great AI engines is the most important.
Its purpose is to compress timelines from design to fully operational 100,000 plus chip deployments, to enable performance improvement by supporting algorithmic achievements. This requires the radical speed and automation of each step to demand the manufacturing model for these infrastructure. From architecture to monitoring and repair, every step should be smooth and automated to take advantage of each hardware generation on an extraordinary scale.
Meeting this moment: a collective attempt for the next generation AI infrastructure
The rise of General AI not only identifies not only an evolution, but also a revolution that requires the fundamentalism of our computing infrastructure. The challenges of ahead – are important in special hardware, integrated networks and sustainable operations – but likewise the AI change capacity will also be viable.
It is easy to see that our results will be unidentified in the next few years, which means that we may not be easily improved in already produced blueprints. Instead, we will have to act on the basis of collective research to the industry, from the first principles to try to re -evaluate the needs of the AI computing, which has to develop a new blueprint for basic global infrastructure. As a result, on the extraordinary scale and performance, from medicine to education to business, it will mainly result in new abilities.
Amin Wahdat is VP and GM for machine learning, system and cloud AI Google Cloud.
