21–30 of 55 posts
Will there be a customer grade TPU in the near future? Or won't they be able to be as price/performance efficient compared to (nvidia) GPUs?
Where are the numbers for the Cerebras chip coming from?:
- How do you have a TDP of 180W for an entire wafer of chips?
- Why is there a peak FP32 number when they are clearly working with FP16?
Each of these chips is a completely different architecture and it makes no sense to compare them at this level. The only meaningful comparison is actual performance in applications because that reflects how the entire system will be used.
Huawei looks like it has a strong game here: "Ascend 910 is used for AI model training. In a typical training session based on ResNet-50, the combination of Ascend 910 and MindSpore is about two times faster at training AI models than other mainstream training cards using TensorFlow." https://www.huawei.com/en/press-events/news/2019/8/Huawei-As... edit: The software framework "MindSpore will go open source in the fir…
I wonder how brittle the performance will be vs other models such as transformers and DRL vs CNN and ResNet.
Quoted from Nvidia Turing datasheet
I am surprised Amazon has not jumped in the game, renting out an accelerator like Google does with the TPUs
AWS gpu compute is extremely expensive. If this is due to datacenter licensing costs, I hope they come out with their own hardware soon to reduce these costs. If on the otherhand, it's because their value-add is not in renting out the hardware but burst scalability, then I'm less optimistic that they'd cannibalize their own cloud product. Currently, it only takes about 1 month to break even if you buy a consumer gpu…
Some of the numbers in that table do not make any sense and makes me question the quality of the entire article. Where are the numbers for the Cerebras chip coming from?: - How do you have a TDP of 180W for an entire wafer of chips? - Why is there a peak FP32 number when they are clearly working with FP16? Each of these chips is a completely different architecture and it makes no sense to compare them at this level.…
15 kW power consumption for 1 chip?!?
https://www.jameswhanlon.com/new-chips-for-machine-intellige...
They claim to have some great features. Anyone know when if a consumer version is coming / any release dates promised?
Are deep neural networks really that widely applicable that it's profitable to design custom chips for them? What about other models of AI that involve, say, discrete math or graph search?
Some of the numbers in that table do not make any sense and makes me question the quality of the entire article. Where are the numbers for the Cerebras chip coming from?: - How do you have a TDP of 180W for an entire wafer of chips? - Why is there a peak FP32 number when they are clearly working with FP16? Each of these chips is a completely different architecture and it makes no sense to compare them at this level.…
One of the numbers that jumped out at me as being very unusual about the Cerebrus chip was this one: "Speculated clock speed of ~1 GHz and 15 kW power consumption." 15 kW power consumption for 1 chip?!?