International audienceGraphics Processing Units (GPUs) offer the possibility to execute floating-point operations (FLOP) with mixed-precisions such as INT8, FP16, Bfloat, FP32, and FP64. For Deep Neural Networks (DNNs), a reduced precision is likely to lower the execution time and power consumption as it requires a smaller hardware area and fewer clock cycles to perform instructions than the standard FP32 and FP64 precisions. As less area is needed for reduced precision, the circuit error rate is also expected to be lower [1]. NVIDIA GPUs also have tensor cores that perform matrix multiplication on hardware. The tensor cores are capable to perform a 4 ×4 FP16 matrix multiplication in one clock cycle [2]. The tensor cores can deliver up to 9...
Mixed-precision (MP) arithmetic combining both single- and half-precision operands has been successf...
In recent years, deep neural networks (DNN) have become one of the most powerful tools in machine le...
Currently, Deep learning and especially Convolutional Neural Networks (CNNs) have become a fundament...
International audienceGraphics Processing Units (GPUs) offer the possibility to execute floating-poi...
Due to limited size, cost and power, embedded devices do not offer the same computational throughput...
The resurgence of machine learning in various applications and it's inherent compute-intensive natur...
Recently, there has been a push to perform deep learning (DL) computations on the edge rather than t...
Emerging deep learning workloads urgently need fast general matrix multiplication (GEMM). To meet su...
Currently, Deep Neural Networks (DNNs) are fun-damental computational structures deployed in a wide ...
Several hardware companies are proposing native Brain Float 16-bit (BF16) support for neural network...
International audienceThe reliability evaluation of Deep Neural Networks (DNNs) executed on Graphic ...
International audienceDeep Neural Networks (DNN) represent a performance-hungry application. Floatin...
Duplication with Comparison (DWC) is an effective software-level solution to improve the reliability...
Deep neural networks have achieved phenomenal successes in vision recognition tasks, which motivate ...
International audienceThe most compute-intensive stage of deep neural network (DNN) training is matr...
Mixed-precision (MP) arithmetic combining both single- and half-precision operands has been successf...
In recent years, deep neural networks (DNN) have become one of the most powerful tools in machine le...
Currently, Deep learning and especially Convolutional Neural Networks (CNNs) have become a fundament...
International audienceGraphics Processing Units (GPUs) offer the possibility to execute floating-poi...
Due to limited size, cost and power, embedded devices do not offer the same computational throughput...
The resurgence of machine learning in various applications and it's inherent compute-intensive natur...
Recently, there has been a push to perform deep learning (DL) computations on the edge rather than t...
Emerging deep learning workloads urgently need fast general matrix multiplication (GEMM). To meet su...
Currently, Deep Neural Networks (DNNs) are fun-damental computational structures deployed in a wide ...
Several hardware companies are proposing native Brain Float 16-bit (BF16) support for neural network...
International audienceThe reliability evaluation of Deep Neural Networks (DNNs) executed on Graphic ...
International audienceDeep Neural Networks (DNN) represent a performance-hungry application. Floatin...
Duplication with Comparison (DWC) is an effective software-level solution to improve the reliability...
Deep neural networks have achieved phenomenal successes in vision recognition tasks, which motivate ...
International audienceThe most compute-intensive stage of deep neural network (DNN) training is matr...
Mixed-precision (MP) arithmetic combining both single- and half-precision operands has been successf...
In recent years, deep neural networks (DNN) have become one of the most powerful tools in machine le...
Currently, Deep learning and especially Convolutional Neural Networks (CNNs) have become a fundament...