148 citations · 428 across the 18 of their papers we have counts for
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Anatomy Of High-Performance Deep Learning Convolutions On SIMD Architectures
Evangelos Georganas, Sasikanth Avancha, Kunal Banerjee +4
Convolution layers are prevalent in many classes of deep neural networks, including Convolutional Neural Networks (CNNs) which provide state-of-the-art results for tasks like image…
Mixed Precision Training of Convolutional Neural Networks using Integer Operations
Dipankar Das, Naveen Mellempudi, Dheevatsa Mudigere +14
The state-of-the-art (SOTA) for mixed precision training is dominated by variants of low precision floating point operations, and in particular, FP16 accumulating into FP32 Micikev…
On Scale-out Deep Learning Training for Cloud and HPC
Srinivas Sridharan, Karthikeyan Vaidyanathan, Dhiraj Kalamkar +8
The exponential growth in use of large deep neural networks has accelerated the need for training these deep neural networks in hours or even minutes. This can only be achieved thr…