Using Recurrent Neural Networks to Learn the Structure of Interconnection Networks

A modified Recurrent Neural Network (RNN) is used to learn a Self-Routing Interconnection Network (SRIN) from a set of routing examples. The RNN is modified so that it has several distinct initial states. This is equivalent to a single RNN learning multiple different synchronous sequential machines. We define such a sequential machine structure as augmented and show that a SRIN is essentially an Augmented Synchronous Sequential Machine (ASSM). As an example, we learn a small six-switch SRIN. After training we extract the network's internal representation of the ASSM and corresponding SRIN. (Also cross-referenced as UMIACS-TR-94-20.

A computing task ergonomic risk assessment tool for assessing risk factors of work related musculoskeletal disorders

Observation method remains to be the most widely applied method in assessing exposure to risk factors for work-related musculoskeletal disorders (WMSDs) related to office works because it is inexpensive and applicable to wide range of office jobs. However, the existing research that applied this method was mainly focused to a limited range of office components and computer accessories such as seat pan, keyboards, mouse, monitor and telephone. In addition, further testing of reliability and validity of the observational method was less reported. This study was conducted to propose the new office ergonomic risk assessment (OFFERA) method to assess a wide range of office risk factors related to WMSDs, which include office components and office environment where this method covers both right and left side of the body part. The initial development of OFFERA method was divided into two stages, the development of OFFERA system components and psychometric properties of OFFERA method. In reliability testing, the results of inter and intra observer reliability recorded good (K=0.62-0.78) and very good (K=0.81-0.96) agreement among the observers. Meanwhile, in validity testing, the relationship of the final score of OFFERA to the musculoskeletal symptoms statistically shows a significant value for wrists/hands (χ²=7.942; p=0.047), lower back (χ²=13.478; p=0.000), knees (χ²=7.001; p=0.008), and ankle/leg (χ²=5.098; p=0.024). The usability testing shows that the OFFERA method was easy and quick to be used (mean 4.48 ± 0.821) and applicable for wide range of office working activities (mean 4.02 ± 0.952). Based on the results obtained, it can be concluded that the OFFERA method was found to be practically reliable and applicable for wide range of office work-related activities

Multilayer optical learning networks

A new approach to learning in a multilayer optical neural network based on holographically interconnected nonlinear devices is presented. The proposed network can learn the interconnections that form a distributed representation of a desired pattern transformation operation. The interconnections are formed in an adaptive and self-aligning fashioias volume holographic gratings in photorefractive crystals. Parallel arrays of globally space-integrated inner products diffracted by the interconnecting hologram illuminate arrays of nonlinear Fabry-Perot etalons for fast thresholding of the transformed patterns. A phase conjugated reference wave interferes with a backward propagating error signal to form holographic interference patterns which are time integrated in the volume of a photorefractive crystal to modify slowly and learn the appropriate self-aligning interconnections. This multilayer system performs an approximate implementation of the backpropagation learning procedure in a massively parallel high-speed nonlinear optical network

E-PUR: An Energy-Efficient Processing Unit for Recurrent Neural Networks

Recurrent Neural Networks (RNNs) are a key technology for emerging applications such as automatic speech recognition, machine translation or image description. Long Short Term Memory (LSTM) networks are the most successful RNN implementation, as they can learn long term dependencies to achieve high accuracy. Unfortunately, the recurrent nature of LSTM networks significantly constrains the amount of parallelism and, hence, multicore CPUs and many-core GPUs exhibit poor efficiency for RNN inference. In this paper, we present E-PUR, an energy-efficient processing unit tailored to the requirements of LSTM computation. The main goal of E-PUR is to support large recurrent neural networks for low-power mobile devices. E-PUR provides an efficient hardware implementation of LSTM networks that is flexible to support diverse applications. One of its main novelties is a technique that we call Maximizing Weight Locality (MWL), which improves the temporal locality of the memory accesses for fetching the synaptic weights, reducing the memory requirements by a large extent. Our experimental results show that E-PUR achieves real-time performance for different LSTM networks, while reducing energy consumption by orders of magnitude with respect to general-purpose processors and GPUs, and it requires a very small chip area. Compared to a modern mobile SoC, an NVIDIA Tegra X1, E-PUR provides an average energy reduction of 92x