PAM4 Transmitter and Receiver Equalizers Optimization for High-Speed Serial Links

As the telecommunications markets evolves, the demand of faster data transfers and processing continue to increase. In order to confront this demand, the peripheral component interconnect express (PCIe) has been increasing the data rates from PCIe Gen 1(4 Gb/s) to PCIe Gen 5(32 Gb/s). This evolution has brought new challenges due to the high-speed interconnections effects which can cause data loss and intersymbol interference. Under these conditions the traditional non return to zero modulation (NRZ) scheme became a bottle neck due to bandwidth limitations in the high-speed interconnects. The pulse amplitude modulation 4-level (PAM4) scheme is been implemented in next generation of PCIe (PCIe6) doubling the data rate without increasing the channel bandwidth. However, while PAM4 solve the bandwidth problem it also brings new challenges in post silicon equalization. Tuning the transmitter (Tx) and receiver (Rx) across different interconnect channels can be a very time-consuming task due to multiple equalizers implemented in the serializer/deserializer (SerDes). Typical current industrial practices for SerDes equalizers tuning require massive lab measurements, since they are based on exhaustive enumeration methods, making the equalization process too lengthy and practically prohibitive under current silicon time-to-market commitments. In this master’s dissertation a numerical method is proposed to optimize the transmitter and receiver equalizers of a PCIe6 link. The experimental results, tested in a MATLAB simulation environment, demonstrate the effectiveness of the proposed approach by delivering optimal PAM4 eye diagrams margins while significantly reducing the jitter.ITESO, A.C

On the Robustness of Explanations of Deep Neural Network Models: A Survey

Explainability has been widely stated as a cornerstone of the responsible and trustworthy use of machine learning models. With the ubiquitous use of Deep Neural Network (DNN) models expanding to risk-sensitive and safety-critical domains, many methods have been proposed to explain the decisions of these models. Recent years have also seen concerted efforts that have shown how such explanations can be distorted (attacked) by minor input perturbations. While there have been many surveys that review explainability methods themselves, there has been no effort hitherto to assimilate the different methods and metrics proposed to study the robustness of explanations of DNN models. In this work, we present a comprehensive survey of methods that study, understand, attack, and defend explanations of DNN models. We also present a detailed review of different metrics used to evaluate explanation methods, as well as describe attributional attack and defense methods. We conclude with lessons and take-aways for the community towards ensuring robust explanations of DNN model predictions.Comment: Under Review ACM Computing Surveys "Special Issue on Trustworthy AI