GRENOBLE, France – Dec. 7, 2023 – A team comprising CEA-Leti, CEA-List and two CNRS laboratories has published a paper in Nature Communications presenting what the authors said is the first complete memristor-based Bayesian neural network implementation for a real-world task — classifying types of arrhythmia recordings with precise aleatoric and epistemic uncertainty. Considering medical-diagnosis and […]
HPC Predictions 2021: Quantum Beyond Qubits; the Rising Composable and Memory-Centric Tide; Neural Network Explosion; 5G’s Slow March
January 4, 2021 by
Annual technology predictions are like years: some are better than others. Nonetheless, many provide useful insight and serve as the basis for worthwhile discussion. Over the last few months we received a number of HPC and AI predictions for 2021, here are the most interesting and potentially valid. Let’s check in 12 months from now […]
Slidecast: Dell EMC Using Neural Networks to “Read Minds”
November 17, 2019 by
In this slidecast, Luke Wilson from Dell EMC describes a case study with McGill University using neural networks to read minds. “If you want to build a better neural network, there is no better model than the human brain. In this project, McGill University was running into bottlenecks using neural networks to reverse-map fMRI images. The team from the Dell EMC HPC & AI Innovation Lab was able to tune the code to run solely on Intel Xeon Scalable processors, rather than porting to the university’s scarce GPU accelerators.”
Deep Learning for Natural Language Processing – Choosing the Right GPU for the Job
October 23, 2019 by
In this new whitepaper from our friends over at Exxact Corporation we take a look at the important topic of deep learning for Natural Language Processing (NLP) and choosing the right GPU for the job. Focus is given to the latest developments in neural networks and deep learning systems, in particular a neural network architecture called transformers. Researchers have shown that transformer networks are particularly well suited for parallelization on GPU-based systems.
New Paper: Survey of FPGAs for Convolutional Neural Networks
September 30, 2018 by
Sparsh Mittal has just published a new paper on the use of FPGAs for Convolutional Neural Networks. “Deep convolutional neural networks (CNNs) have recently shown very high accuracy in a wide range of cognitive tasks and due to this, they have received significant interest from tnewhe researchers. Given the high computational demands of CNNs, custom hardware accelerators are vital for boosting their performance. The high energy-efficiency, computing capabilities and reconfigurability of FPGA make it a promising platform for hardware acceleration of CNNs.”
A Survey of FPGA-based Accelerators for Convolutional Neural Networks
September 30, 2018 by Leave a Comment
Deep convolutional neural networks (CNNs) have recently shown very high accuracy in a wide range of cognitive tasks and due to this, they have received significant interest from the researchers. Given the high computational demands of CNNs, custom hardware accelerators are vital for boosting their performance. The high energy-efficiency, computing capabilities and reconfigurability of FPGA make it a promising platform for hardware acceleration of CNNs. In this paper, we present a survey of techniques for implementing and optimizing CNN algorithms on FPGA. We organize the works in several categories to bring out their similarities and differences. This paper is expected to be useful for researchers in the area of artificial intelligence, hardware architecture and system-design.
The Machine Learning Potential of a Combined Tech Approach
April 23, 2018 by
This is the first in a five-part series from a report exploring the potential of unified deep learning with CPU, GPU and FGPA technologies. This post explores the machine learning potential of taking a combined approach to these technologies.
MIT helps move Neural Nets back to Analog
February 14, 2018 by
MIT researchers have developed a special-purpose chip that increases the speed of neural-network computations by three to seven times over its predecessors, while reducing power consumption 94 to 95 percent. “The computation these algorithms do can be simplified to one specific operation, called the dot product. Our approach was, can we implement this dot-product functionality inside the memory so that you don’t need to transfer this data back and forth?”
TensorFlow Deep Learning Optimized for Modern Intel Architectures
August 31, 2017 by
Researchers at Google and Intel recently collaborated to extract the maximum performance from Intel® Xeon and Intel® Xeon Phi processors running TensorFlow*, a leading deep learning and machine learning framework. This effort resulted in significant performance gains and leads the way for ensuring similar gains from the next generation of products from Intel. Optimizing Deep Neural Network (DNN) models such as TensorFlow presents challenges not unlike those encountered with more traditional High Performance Computing applications for science and industry.
Deep Learning Frameworks Get a Performance Benefit from Intel MKL Matrix-Matrix Multiplication
June 22, 2017 by
Intel® Math Kernel Library 2017 (Intel® MKL 2017) includes new GEMM kernels that are optimized for various skewed matrix sizes. The new kernels take advantage of Intel® Advanced Vector Extensions 512 (Intel® AVX-512) and achieves high GEMM performance on multicore and many-core Intel® architectures, particularly for situations arising from deep neural networks..
