Tothemoon24
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Nvidia’s H100 AI #GPUs are taking the tech world by storm, but their reign comes at the price of a hefty energy bill.
According to a report from #CBInsights and #Stocklytics.com, these power-hungry processors are projected to consume a staggering 13,797 GWh in 2024, exceeding the annual energy consumption of nations like #Georgia and #CostaRica.
Imagine this, a legacy data center consumes 10 kW/rack where #CyrusOne, #KKR owned leading global data center operator and developer specialising in #AI applications, consumes 300 kW/rack!
But why do #GPUs consume so much power?
Data center #GPUs consume a substantial amount of power primarily due to their high computational requirements and the complex algorithms they handle. These #GPUs optimize parallel processing tasks like #machinelearning and #dataanalytics, involving simultaneous processing of vast amounts of data.
While #parallel processing speeds up data processing, one demerit is that, at a time most parts of a chip are active. This constant computation, coupled with the execution of complex algorithms, demands significant computational power, thereby increasing energy consumption.
The large-scale deployment of #GPUs in data centers, where racks and clusters utilize hundreds or thousands of #GPUs further amplify their collective power consumption. This combination of factors underscores the considerable energy consumption associated with data center GPUs.
Successfully navigating these challenges and fostering innovation will shape the future landscape of #AI computing.
So, what options do we have?
● #Amazon, frenemy to Nvidia, recently unveiled Arm based Graviton4 and Trainium2 chips holds promise for efficiency gains.
● In the near to medium term, #Neuromorphic computing is being researched aggressively as an alternative to synchronous parallel computing architectures. Neuromorphic computing is an asynchronous computing paradigm which runs on event based ‘spikes’ rather than a clock signal. And drastically lowers the power consumption.
● Big money is going into enabling tech like liquid cooling - #KKR acquired CoolIT Systems for $270 mn and Bosch acquired Jetcool through its venture arm
While CooIT Systems becomes the supplier for Cyrus One, #KKR makes money on both!
and ear
can process complex and dynamic scenes with such high speed and accuracy? Imagine if we could design artificial sensors that mimic the biological mechanisms of vision and hearing, and produce data that is more efficient and meaningful than conventional sensors.
- "In simpler terms, these special sensors work like our eyes and ears. They're designed based on the way our eyes' retinas and ears' cochleae function. But what sets them apart is their unique approach called event-based sensing. Unlike regular sensors that take pictures or recordings at a set speed, these event-based sensors only provide information when there's a change. Whether it's a shift in light or a change in sound, they only capture those moments. Instead of a constant flow of data, you get quick updates that show when and where things change. This gives you highly detailed and fast information about what's happening, with minimal delay and a wide range of details. It's like having sensors that focus on the important stuff, making them efficient and responsive."
, a dynamic range of 120 dB
, and a low power consumption of 30 mW
. The DVS can also avoid motion blur and under/overexposure that plague conventional cameras. The DVS can be used for applications such as optical flow estimation, object tracking, gesture recognition, and robotics.
, a dynamic range of 60 dB
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