Power estimation for customized accelerators, especially those derived from high-level programming languages, entails the invocation of a long electronic design automation (EDA) tool chain, thus incurring large timing overhead that hinders early design optimization. To mitigate this problem, in this paper, we propose HIPPO, an architecture-level power modeling framework for field-programmable gate arrays (FPGAs). HIPPO operates directly on C/C++ programs, whose execution is prior to and independent of any EDA tool including the very front-end, high-level synthesis (HLS). During power modeling, HIPPO exploits the intrinsic C/C++ code hierarchies, including nested loops and operations, and enables multi-level power estimation that aligns with different code hierarchies. Specifically, HIPPO can be decomposed into (1) a code transformation flow that directly converts a C/C++ program with HLS pragmas into hardware-oriented and power-aware control and dataflow graph, (2) a hierarchy-preserving power modeling methodology that combines analytical modeling and data-driven learning approaches to effectively orchestrate different code hierarchies, and (3) an adaptive dataflow coarsening strategy which ensures modeling accuracy, efficiency and robustness by suppressing noise of onboard measurement. Experimental results demonstrate that HIPPO effectively decomposes and accurately predicts both dynamic and total power consumption, achieving average errors of 8.89% (dynamic) and 6.31% (total) for nested loops, and 9.86% (dynamic) and 3.41% (total) for single loops, respectively. These results prove that HIPPO paves the way for power-efficient high-level architecture exploration.

Federated learning allows multiple clients to collaboratively train a global model without revealing their private data. Despite its success in many applications, it remains a challenge to prevent malicious clients to corrupt the global model through uploading incorrect model updates. Hence, one critical issue arises in how to validate the training is truly conducted on legitimate neural networks. To address the issue, we proposeVPNNT, a zero-knowledge proof scheme for neural network backpropagation.VPNNTenables each client to prove to others that the model updates (gradients) are indeed calculated on the global model of the previous round, without leaking any information about the client's private training data. Our proof scheme is generally applicable to any type of neural network. Different from conventional verification schemes constructing neural network operations by gate-level circuits, we improve verification efficiency by formulating the training process using custom gates — matrix operations, and apply an optimized linear time zero knowledge protocol for verification. Thanks to the recursive structure of neural network backward propagation, common custom gates are combined in verification thereby reducing prover and verifier costs over conventional zero knowledge proofs. Experimental results show thatVPNNTis a lightweighted verification scheme for neural network backpropagation with an improved prove time, verification time and proof size.

Background: Metamorphic testing emerges as a simple and effective approach for testing scientific software; yet, its adoption in actual scientific software projects is less studied.

Abstraction identification is aimed at discovering significant domain terms. Prior work, notably AbstFinder and RAI (relevance-driven abstraction identification), has introduced the core ideas, but offered only limited tool support. This paper presents our abstraction identification tool, Co-AI, built on the Google Colab environment allowing the users to run the tool within their web browsers, promoting tool adoption and extension. Co-AI integrates the Wikipedia pages as the domain corpus, and identifies the candidate abstractions with a set of natural language processing (NLP) patterns. Co-AI is available at: https://colab.research.google.com/drive/1ur5KILoi_n-3KY0_vJcMBQDtiSYgcYeP?usp=sharing and we welcome the community’s feedback of our tool.

Abstractions are significant domain terms that have assisted in requirements elicitation and modeling. To extend the assistance towards requirements validation, we present in this paper an automated approach to identifying the abstractions for supporting requirements-based testing. We select relevant Wikipedia pages to serve as a domain corpus that is independent from any specific software system. We further define five novel patterns based on part-of-speech tagging and dependency parsing, and frame our candidate abstractions in the form ofpairs for better testability. We evaluate our approach with six software systems in two application domains: Electronic health records and Web conferencing. The results show that our abstractions are more accurate than those generated by two of the state-of-the-art techniques. Initial findings also indicate our abstractions’ capabilities of revealing bugs and matching the environmental assumptions created manually.