124 citations · 207 across the 7 of their papers we have counts for
8 papers
Exploring Alternatives to Softmax Function
Kunal Banerjee, Vishak Prasad C, Rishi Raj Gupta +3
Softmax function is widely used in artificial neural networks for multiclass classification, multilabel classification, attention mechanisms, etc. However, its efficacy is often qu…
K-TanH: Efficient TanH For Deep Learning
Abhisek Kundu, Alex Heinecke, Dhiraj Kalamkar +7
We propose K-TanH, a novel, highly accurate, hardware efficient approximation of popular activation function TanH for Deep Learning. K-TanH consists of parameterized low-precision…
High-Performance Deep Learning via a Single Building Block
Evangelos Georganas, Kunal Banerjee, Dhiraj Kalamkar +6
Deep learning (DL) is one of the most prominent branches of machine learning. Due to the immense computational cost of DL workloads, industry and academia have developed DL librari…
A Study of BFLOAT16 for Deep Learning Training
Dhiraj Kalamkar, Dheevatsa Mudigere, Naveen Mellempudi +16
This paper presents the first comprehensive empirical study demonstrating the efficacy of the Brain Floating Point (BFLOAT16) half-precision format for Deep Learning training acros…
A Quick Introduction to Functional Verification of Array-Intensive Programs
Kunal Banerjee, Chandan Karfa
Array-intensive programs are often amenable to parallelization across many cores on a single machine as well as scaling across multiple machines and hence are well explored, especi…
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…