Yinghao Yang

Shangyi Shi, Husheng Han, Zhaoxuan Kan et al.

CKKS, an emerging fully homomorphic encryption (FHE) scheme, has been promising in privacy-preserving applications by enabling SIMD fixed-point computations on ciphertexts. Despite its strong security guarantees, CKKS involves both compute-intensive operators (ComOps) with high computational cost and memory-intensive operators (MemOps) with large memory footprints, making existing ASIC-based or NMP-based acceleration approaches suffer from high hardware overhead and limited efficiency. This observation motivates the integration of the architectural advantages of both paradigms into a heterogeneous xPU (ASIC)-xMU (NMP) architecture. However, in such a design, frequent and long-latency heterogeneous communication caused by the dominant keyswitch operator remains a key performance bottleneck. In this paper, we propose $HE^2$, a communication-light xPU-xMU heterogeneous FHE accelerator with dataflow graph (DFG) optimization and architecture co-design. First, we observe that the majority of communication arises at the interface between ModUp/ModDown and neighboring MemOps. To address this, we propose a DFG-level optimization framework to fully exploit the ModUp/ModDown reduction potential of the hoisting algorithm by identifying parallel keyswitch blocks and fusing them for reduced communication frequency. Second, we design an efficient heterogeneous architecture that adopts a group-level pipelined execution to effectively hide communication latency by leveraging the inherent parallelism across decomposed groups. End-to-end evaluation results show that $HE^2$ achieves 1.66$\times$ speedup and 9.23$\times$ lower EDAP (Energy-Delay-Area Product) compared to the state-of-the-art accelerator, with communication stalls accounting for only 6.67% of the total latency.

Yulong Chen, Yang Liu, Jianhao Yan et al. · cambridge, tencent-ai

The impressive performance of Large Language Models (LLMs) has consistently surpassed numerous human-designed benchmarks, presenting new challenges in assessing the shortcomings of LLMs. Designing tasks and finding LLMs' limitations are becoming increasingly important. In this paper, we investigate the question of whether an LLM can discover its own limitations from the errors it makes. To this end, we propose a Self-Challenge evaluation framework with human-in-the-loop. Starting from seed instances that GPT-4 fails to answer, we prompt GPT-4 to summarize error patterns that can be used to generate new instances and incorporate human feedback on them to refine these patterns for generating more challenging data, iteratively. We end up with 8 diverse patterns, such as text manipulation and questions with assumptions. We then build a benchmark, SC-G4, consisting of 1,835 instances generated by GPT-4 using these patterns, with human-annotated gold responses. The SC-G4 serves as a challenging benchmark that allows for a detailed assessment of LLMs' abilities. Our results show that only 44.96\% of instances in SC-G4 can be answered correctly by GPT-4. Interestingly, our pilot study indicates that these error patterns also challenge other LLMs, such as Claude-3 and Llama-3, and cannot be fully resolved through fine-tuning. Our work takes the first step to demonstrate that LLMs can autonomously identify their inherent flaws and provide insights for future dynamic and automatic evaluation.