category: AI/ML/DL

category: IoT, Security & Automotive

RaPiD: AI Accelerator for Ultra-low Precision Training and Inference
By Technical Paper Link - 02 Jul, 2021 - Comments: 0

Abstract—"The growing prevalence and computational demands of Artificial Intelligence (AI) workloads has led to widespread use of hardware accelerators in their execution. Scaling the performance of AI accelerators across generations is pivotal to their success in commercial deployments. The intrinsic error-resilient nature of AI workloads present a unique opportunity for performance/energy i... » read more

NN-Baton: DNN Workload Orchestration & Chiplet Granularity Exploration for Multichip Accelerators
By Technical Paper Link - 14 Jun, 2021 - Comments: 0

"Abstract—The revolution of machine learning poses an unprecedented demand for computation resources, urging more transistors on a single monolithic chip, which is not sustainable in the Post-Moore era. The multichip integration with small functional dies, called chiplets, can reduce the manufacturing cost, improve the fabrication yield, and achieve die-level reuse for different system scales... » read more

Communication Algorithm-Architecture Co-Design for Distributed Deep Learning
By Technical Paper Link - 14 Jun, 2021 - Comments: 0

"Abstract—Large-scale distributed deep learning training has enabled developments of more complex deep neural network models to learn from larger datasets for sophisticated tasks. In particular, distributed stochastic gradient descent intensively invokes all-reduce operations for gradient update, which dominates communication time during iterative training epochs. In this work, we identify th... » read more

Ten Lessons From Three Generations Shaped Google’s TPUv4i
By Technical Paper Link - 11 Jun, 2021 - Comments: 0

Source: Norman P. Jouppi, Doe Hyun Yoon, Matthew Ashcraft, Mark Gottscho, Thomas B. Jablin, George Kurian, James Laudon, Sheng Li, Peter Ma, Xiaoyu Ma, Nishant Patil, Sushma Prasad, Clifford Young, Zongwei Zhou (Google); David Patterson (Google / Berkeley) Find technical paper here. 2021 ACM/IEEE 48th Annual International Symposium on Computer Architecture (ISCA) Abstract–"Google de... » read more

HyperRec: Efficient Recommender Systems with Hyperdimensional Computing
By Technical Paper Link - 12 May, 2021 - Comments: 0

A group of researchers are taking a different approach to AI. The University of California at San Diego, the University of California at Irvine, San Diego State University and DGIST recently presented a paper on a new hardware algorithm based on hyperdimensional (HD) computing, which is a brain-inspired computing model. The new algorithm, called HyperRec, uses data that is modeled with bina... » read more

Final Report: National Security Commission on AI
By Technical Paper Link - 10 Apr, 2021 - Comments: 0

  In August 2018, Section 1051 of the John S. McCain National Defense Authorization Act for Fiscal Year 2019 established the National Security Commission on Artificial Intelligence as an independent Commission “to consider the methods and means necessary to advance the development of artificial intelligence, machine learning, and associated technologies to comprehensively address the... » read more

Enabling Efficient and Flexible FPGA Virtualization for Deep Learning in the Cloud
By Technical Paper Link - 06 Mar, 2021 - Comments: 0

SOURCE: Shulin Zeng, Guohao Dai, Hanbo Sun, Kai Zhong, Guangjun Ge, Kaiyuan Guo, Yu Wang, Huazhong Yang(Tsinghua University, Beijing, China).  Published on arXiv:2003.12101 [cs.DC])   ABSTRACT: "FPGAs have shown great potential in providing low-latency and energy-efficient solutions for deep neural network (DNN) inference applications. Currently, the majority of FPGA-based DNN accel... » read more

Learning properties of ordered and disordered materials from multi-fidelity data
By Technical Paper Link - 14 Jan, 2021 - Comments: 0

Source: Chen, C., Zuo, Y., Ye, W. et al. Learning properties of ordered and disordered materials from multi-fidelity data. Nat Comput Sci 1, 46–53 (2021). https://doi.org/10.1038/s43588-020-00002-x Abstract: "Predicting the properties of a material from the arrangement of its atoms is a fundamental goal in materials science. While machine learning has emerged in recent years as a n... » read more

Improving the Performance Of Deep Neural Networks
By Technical Paper Link - 11 Jan, 2021 - Comments: 0

Source: North Carolina State University. Authors: Xilai Li, Wei Sun, and Tianfu Wu Abstract: "In state-of-the-art deep neural networks, both feature normalization and feature attention have become ubiquitous. They are usually studied as separate modules, however. In this paper, we propose a light-weight integration between the two schema and present Attentive Normalization (AN). Instead of l... » read more

AI Roadmap: A human-centric approach to AI in aviation
By Technical Paper Link - 09 Mar, 2020 - Comments: 0

Source: EASA European Union Aviation Safety Agency February 2020 "EASA published its Artificial Intelligence Roadmap 1.0 which establishes the Agency’s initial vision on the safety and ethical dimensions of development of AI in the aviation domain. The AI Roadmap 1.0 is to be viewed as a starting point, intended to serve as a basis for discussion with the Agency’s stakeholders. It... » read more

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