Google TPU2 delivers 45Tflops
- 저자:Ella Cai
- 에 출시:2017-05-18
Google has produced a new version of its Tensor Flow Processor (TPU) aimed at machine learning in the Cloud. Google calls it TPU2 or Cloud TPU.
A single TPU2 delivers 45Tflops. A system board with four TPU2s delivers 180Tflops and a customised network of 64 boards called a TPU pod delivers 11.5 petaflops.
“Our second-generation Tensor Processing Units (TPUs) are coming to Google Cloud to accelerate a wide range of machine learning workloads, including both training and inference,” says Google, ” these breakthroughs required enormous amounts of computation, both to train the underlying machine learning models and to run those models once they’re trained (this is called ‘inference’. ”
“We’re bringing our new TPUs to Google Compute Engine as Cloud TPUs, where you can connect them to virtual machines of all shapes and sizes and mix and match them with other types of hardware, including Skylake CPUs and NVIDIA GPUs,” says Google, “youu can program these TPUs with TensorFlow and we’re introducing high-level APIs, which will make it easier to train machine learning models on CPUs, GPUs or Cloud TPUs with only minimal code changes.”
“With Cloud TPUs, you have the opportunity to integrate state-of-the-art ML accelerators directly into your production infrastructure and benefit from on-demand, accelerated computing power without any up-front capital expenses,” says Google, “since fast ML accelerators place extraordinary demands on surrounding storage systems and networks, we’re making optimizations throughout our Cloud infrastructure to help ensure that you can train powerful ML models quickly using real production data.”
A single TPU2 delivers 45Tflops. A system board with four TPU2s delivers 180Tflops and a customised network of 64 boards called a TPU pod delivers 11.5 petaflops.
“Our second-generation Tensor Processing Units (TPUs) are coming to Google Cloud to accelerate a wide range of machine learning workloads, including both training and inference,” says Google, ” these breakthroughs required enormous amounts of computation, both to train the underlying machine learning models and to run those models once they’re trained (this is called ‘inference’. ”
“We’re bringing our new TPUs to Google Compute Engine as Cloud TPUs, where you can connect them to virtual machines of all shapes and sizes and mix and match them with other types of hardware, including Skylake CPUs and NVIDIA GPUs,” says Google, “youu can program these TPUs with TensorFlow and we’re introducing high-level APIs, which will make it easier to train machine learning models on CPUs, GPUs or Cloud TPUs with only minimal code changes.”
“With Cloud TPUs, you have the opportunity to integrate state-of-the-art ML accelerators directly into your production infrastructure and benefit from on-demand, accelerated computing power without any up-front capital expenses,” says Google, “since fast ML accelerators place extraordinary demands on surrounding storage systems and networks, we’re making optimizations throughout our Cloud infrastructure to help ensure that you can train powerful ML models quickly using real production data.”
