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" ... However, the NVIDIA A100 outperforms the MI100 for AI intensive workloads that typically take advantage of "quantization," the utilization of lower precision formats and operators in tensor operations. I had many discussions with AMD about this data, and they correctly pointed out that the TF32 format, which only has the precision of FP16 (10 bits) is not the same as the IEEE standard FP32 format (23 bits). TF32 has 8 bits of mantissa, just like FP32. However, NVIDIA has not published FP32 FLOPS on Tensor Cores, only TF32, so that is the only comparison I can make for now. AMD also said that most customers it talks with have built models that use IEEE FP32 for training, not BFloat16 or TF32. That may be true—however, in my experience, many HPC and AI implementation teams will refactor their code to take advantage of the massive performance increases that Tensor Cores and TF32 offer. ... "
" ... It’s a common practice to apply quantization - a process to optimize the fully trained model for inference by replacing higher bit data type such as FP32 and FP16 with INT8 and INT4. The quantization technique speeds up the forward propagation of deep learning neural networks during inference. Ampere also supports data types such as bfloat16, INT8 and INT4 typically used for inference. The ability to accelerate both training and inference of deep learning models makes Ampere the most versatile GPU in the industry. ... "
" ... Lobe has an in-built optimization tool that can optimize the model for speed or accuracy. Behind the scenes, it will perform a quantization technique that adjusts the data that is optimal for the CPU. ... "
" ... The upgrades to sensAI include a performance boost of 10 times over the previous edition, an expansion of neural network and ML frameworks support, simple neural network debugging via USB, support for quantization and fraction setting schemes, and new customizable reference designs. Lattice also highlighted sensAI’s growing design service partner ecosystem at the event. ... "
" ... This histogram, from 2007, shows the number of discovered quasars (y-axis) as a function of redshift ... [+] (x-axis). Note that the redshifts of these objects form a continuous distribution, and that there is no evidence of quasar redshift quantization. This overwhelming data completely undermines one of the Big Bang's most serious challenges of the late 20th century. ... "