Stochastic Computing Convolutional Neural Network Architecture Reinvented for Highly Efficient Artificial Intelligence Workload on Field-Programmable Gate Array.

Stochastic computing (SC) has a substantial amount of study on application-specific integrated circuit (ASIC) design for artificial intelligence (AI) edge computing, especially the convolutional neural network (CNN) algorithm. However, SC has little to no optimization on field-programmable gate array (FPGA). Scaling up the ASIC logic without FPGA-oriented designs is inefficient, while aggregating thousands of bitstreams is still challenging in the conventional SC.

Aryl fluorosulfates: powerful and versatile partners in cross-coupling reactions.

Aryl fluorosulfates are versatile building blocks in organic synthesis and have gained increasing attention in SuFEx (Sulfur Fluoride Exchange) click chemistry. They are easily and conveniently prepared from phenols using sulfuryl fluoride SOF as a low-cost sulfonyl fluoride provider. Recently, they served as less toxic and more atom economical alternatives to triflates in an impressive number of carbon-carbon and carbon-heteroatom cross-coupling reactions.

Stochastic computing in convolutional neural network implementation: a review.

Stochastic computing (SC) is an alternative computing domain for ubiquitous deterministic computing whereby a single logic gate can perform the arithmetic operation by exploiting the nature of probability math. SC was proposed in the 1960s when binary computing was expensive. However, presently, SC started to regain interest after the widespread of deep learning application, specifically the convolutional neural network (CNN) algorithm due to its practicality in hardware implementation.

Intelligent color vision system for ripeness classification of oil palm fresh fruit bunch.

Ripeness classification of oil palm fresh fruit bunches (FFBs) during harvesting is important to ensure that they are harvested during optimum stage for maximum oil production. This paper presents the application of color vision for automated ripeness classification of oil palm FFB. Images of oil palm FFBs of type DxP Yangambi were collected and analyzed using digital image processing techniques.