Neural networks

The development of neural networks has now reached the stage where they are employed in a large variety of practical contexts. However, to date the majority of such implementations have been in software. While it is generally recognised that hardware implementations could, through performance advantages, greatly increase the use of neural networks, to date the relatively high cost of developing Application-Specific Integrated Circuits (ASICs) has meant that only a small number of hardware neurocomputers has gone beyond the research-prototype stage. The situation has now changed dramatically: with the appearance of large, dense, highly parallel FPGA circuits it has now become possible to envisage putting large-scale neural networks in hardware, to get high performance at low costs. This in turn makes it practical to develop hardware neural-computing devices for a wide range of applications, ranging from embedded devices in high-volume/low-cost consumer electronics to large-scale stand-alone neurocomputers. Not surprisingly, therefore, research in the area has recently rapidly increased, and even sharper growth can be expected in the next decade or so. Nevertheless, the many opportunities offered by FPGAs also come with many challenges, since most of the existing body of knowledge is based on ASICs (which are not as constrained as FPGAs). These challenges range from the choice of data representation, to the implementation of specialized functions, through to the realization of massively parallel neural networks; and accompanying these are important secondary issues, such as development tools and technology transfer. All these issues are currently being investigated by a large number of researchers, who start from different bases and proceed by different methods, in such a way that there is no systematic core knowledge to start from, evaluate alternatives, validate claims, and so forth. FPGA Implementations of Neural Networks aims to be a timely one that fill this gap in three ways: First, it will contain appropriate foundational material and therefore be appropriate for advanced students or researchers new to the field. Second, it will capture the state of the art, in both depth and breadth and therefore be useful researchers currently active in the field. Third, it will cover directions for future research, i.e. embryonic areas as well as more speculative ones.

FPGA Implementations of Neural Networks

Review article: Review of pulse-coupled neural networks

Supervised learning in spiking neural networks: A review of algorithms and evaluations

Challenges for large-scale implementations of spiking neural networks on FPGAs

http://apt.cs.manchester.ac.uk/intranet/csonly/nn/science.pdf

Simulation of Spiking Neural Networks – Architectures and Implementations

Artificial Intelligence in Laser Powder Bed Fusion Procedures – Neural Networks for Live-Detection and Forecasting of Printing Failures.

: With emerging technologies such as high-precision Laser Powder Bed Fusion (LPBF), rapid prototyping has gained remarkable importance in metal manufacturing. Furthermore, cloud computing and easy-to-integrate sensors have boosted the development of digital twins. Such digital twins use data from sensors on physical objects, to improve the understanding of manufacturing processes as a whole or of certain production parameters. That way, digital twins can demonstrate the impact of design changes, usage scenarios, environmental conditions or similar variables. One important application of such digital twins lies in early detection of manufacturing faults, such that real prototypes need to be used less. This reduces development times and allows products to be individually, affordable, powerful, robust and environmentally friendly. While typically simple USB-camera setups or melt-pool imaging are used for this task, most solutions are difficult to integrate into existing processes and hard to calibrate and evaluate. We propose a digital-twin-based solution, that leverages information from camera-images in a self-supervised fashion, and creates a heat transfer based AI quality monitoring. For that purpose, artificially generated labels and physics simulation were combined with a multi-sensor setup and supervised learning. Our model detects printing issues at more than 91% accuracy.

Multi-Modal Artificial Intelligence in Additive Manufacturing: Combining Thermal and Camera Images for 3D-Print Quality Monitoring

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Papers

Simulation of Spiking Neural Networks – Architectures and Implementations

Artificial Intelligence in Laser Powder Bed Fusion Procedures – Neural Networks for Live-Detection and Forecasting of Printing Failures.

Multi-Modal Artificial Intelligence in Additive Manufacturing: Combining Thermal and Camera Images for 3D-Print Quality Monitoring