2D Parity Product Code for TSV online fault correction and detection

Through-Silicon-Via (TSV) is one of the most promising technologies to realize 3D Integrated Circuits (3D-ICs).  However, the reliability issues due to the low yield rates and the sensitivity to thermal hotspots and stress issues are preventing TSV-based 3D-ICs from being widely and efficiently used. To enhance the reliability of TSV connections, using error correction code to detect and correct faults automatically has been demonstrated as a viable solution.This paper presents a 2D Parity Product Code (2D-PPC) for TSV fault-tolerance with the ability to correct one fault and detect, at least, two faults.  In an implementation of 64-bit data and 81-bit codeword, 2D-PPC can detect over 71 faults, on average. Its encoder and decoder decrease the overall latency by 38.33% when compared to the Single Error Correction Double Error Detection code.  In addition to the high detection rates, the encoder can detect 100% of its gate failures, and the decoder can detect and correct around 40% of its individual gate failures. The squared 2D-PPC could be extended using orthogonal Latin square to support extra bit correction

An Energy-Efficient High-Throughput Mesh-Based Photonic On-Chip Interconnect for Many-Core Systems

Future high-performance embedded and general purpose processors and systems-on-chip are expected to combine hundreds of cores integrated together to satisfy the power and performance requirements of large complex applications. As the number of cores continues to increase, the employment of low-power and high-throughput on-chip interconnect fabrics becomes imperative. In this work, we present a novel mesh-based photonic on-chip interconnect, named PHENIC-II, for future high-performance many-core systems. The novel architecture is based on an energy-efficient non-blocking photonic switch and a contention-aware routing algorithm. Simulation results show that the proposed system provides better bandwidth and energy efficiency when compared to conventional hybrid photonic NoC systems

Hybrid Silicon-Photonic Network-on-Chip for Future Generations of High-performance Many-core Systems

International audiencePhotonic Networks-on-Chip (PNoCs) promise significant advantages over their electronic counterparts. In particular, they offer a potentially disruptive technology solution with fundamentally low power dissipation that remains independent of capacity while providing ultra-high throughput and minimal access latency. In conventional hybrid PNoC systems, several electrical control functions, such as path setup, acknowledgment and tear-down are necessary for the end-to-end optical transfer. However

Study of Deep Learning-based Hand Gesture Recognition Toward the Design of a Low-cost Prosthetic Hand

Background: The surface EMG (sEMG) signal is inherently noisy and, therefore, not a robust input source for prosthetic systems, especially for fatigue, electrode displacement, and sweat conditions. We propose to address these issues by designing a multi-modal approach that combines vision and EMG empowered with appropriate dataset collection. Methods: In Frame-based, the machine learning model used for recognition was a 2D-CNN. The data is image data that is input to the model by preparing videos showing 10 patterns of hand gestures along with multiple backgrounds, and dividing these videos into frames. These image data are then pre-processed and input to the machine learning model. The model is then evaluated in terms of the accuracy of hand gesture identification using the test data and the loss value, which represents the error between the expected data and the correct data output. In EMG, the Myo armband is placed on the forearm and the sEMG of 200 (Hz) is measured. There are six patterns of hand gestures in this process. Similar to the images, these sEMG data are preprocessed and input to a machine learning model for classification. The model is evaluated the model by the accuracy of hand gesture identification using the test data and the loss value, precision, recall , F1-score. Results: The value of the loss function in case of frame-based was 0.0770 and the accuracy was 0.9739 at 1000 epochs of the training data. And the value of the loss function values in the test data were 0.1011 for the loss value and 0.9657 for the accuracy. In the case of EMG, the loss value was 0.931 when the time to maintain the gesture was the longest, and the loss value was 0.171. However, Precision, Recall, and F1-score were not the highest at the longest time for some gestures. Conclusion: In this paper, we created a hand gesture identification software using Frame-based and sEMG, and measured its accuracy and loss value. For sEMG, we used Precision, Recall, and F1-score to check the metrics of each gesture identification. The frame-based results showed good results in both precision and loss values. sEMG showed an improvement in precision and loss values as the time length increased, but there was a tendency to decrease in some indices. In the future, it is necessary to explore the local relationship between finger and forearm to optimize out learning model