The wide adoption of DNNs has given birth to unrelenting computing requirements, forcing
datacenter operators to adopt domain-specific accelerators to train them. These accelerators …

When training deep neural networks (DNNs), expensive floating point arithmetic units are
used in GPUs or custom neural processing units (NPUs). To reduce the burden of floating …

Block Floating Point (BFP) can efficiently support quantization for Deep Neural Network
(DNN) training by providing a wide dynamic range via a shared exponent across a group of …

Reducing the numerical precision of data and computation is extremely effective in
accelerating deep learning training workloads. Towards this end, 8-bit floating point …

Training Deep Neural Networks (DNN) with high efficiency can be difficult to achieve with
native floating-point representations and commercially available hardware. Specialized …

The state-of-the-art hardware platforms for training deep neural networks are moving from
traditional single precision (32-bit) computations towards 16 bits of precision-in large part …

The resilience of Deep Learning (DL) training and inference workloads to low-precision
computations, coupled with the demand for power-and area-efficient hardware accelerators …

Deep neural networks (DNN) have demonstrated effectiveness for various applications such
as image processing, video segmentation, and speech recognition. Running state-of-the-art …

Deep neural network (DNN) quantization converting floating-point (FP) data in the network
to integers (INT) is an effective way to shrink the model size for memory saving and simplify …

While Deep Neural Networks (DNNs) push the state-of-the-art in many machine learning
applications, they often require millions of expensive floating-point operations for each input …