arXiv subjects

Yu Hin Chan

Publications and source records attributed to Yu Hin Chan.

5 recordsLinked to original sources

arXiv2026

SAGE: Semantic-Aware Geographic Error Recovery for AI Data Movement

AI interconnects typically protect and replay packets uniformly, yet numerical bit faults differ sharply in consequence: a low-order mantissa flip may resemble quantization noise, while a high-significance exponent flip can produce a catastrophic outlier or non-finite value. We present SAGE, a semantic-aware geographic error-recovery architecture that decouples whether a detected fault merits replay from where replay restarts. For BF16-like data, a workload-calibrated contract separates catastrophic Class-H faults from bounded Class-M and precision Class-L damage. It first applies a Class-H silent-delivery constraint, then ranks admissible policies by quality-normalized terminal latency, $Ψ_{\rm del}$. Independently, a source-local region table adapts checkpoint intervals to fault geography, shortening recovery segments in noisy regions. Detected Class-H failures may trigger protected negative acknowledgments and full-flit replay; Class-M and Class-L outcomes do not trigger default network replay. We implement SAGE's endpoint and replay protocol in gem5 Garnet and synthesize its fully pipelined checker in ASAP7. At a stable synthetic operating point, a ten-seed contention-faithful direct-Garnet campaign shows that SAGE reduces $Ψ_{\rm del}$ by 30.1% relative to fixed 34-hop recovery, combining 28.0% lower mean latency with improved delivered semantic quality. Under higher-BER synthetic stress at the same offered load, SAGE maintains bounded queues while the fixed baseline accumulates backlog. Application-derived DeiT-S communication traces also show lower mean and p99.5 latency at the evaluated nonzero BERs. Within the qualified operating envelope, CRC32 decoder trials yield a simultaneous 95% per-original Class-H silent-delivery upper bound of $3.18\times10^{-7}$.

cs.AR↗

arXiv2026

SwiftChannel: Algorithm-Hardware Co-Design for Deep Learning-Based 5G Channel Estimation

Channel estimation is crucial in 5G communication networks for optimizing transmission parameters and ensuring reliable, high-speed communication. However, the use of multiple-input and multiple-output (MIMO) and millimeter-wave (mmWave) in 5G networks presents challenges in achieving accurate estimation under strict latency requirements on resource-limited hardware platforms. To address these challenges, we propose SwiftChannel, an algorithm-hardware co-design framework that integrates a hardware-friendly deep learning-based channel estimator with a dedicated accelerator. Our approach employs a convolutional neural network enhanced with a parameter-free attention mechanism, which effectively reconstructs full-resolution spatial-frequency domain channel matrices from low-resolution least squares (LS) estimates. We further develop a multi-stage model compression pipeline combining knowledge distillation, convolution re-parameterization, and quantization-aware training, resulting in substantial model size reduction with negligible accuracy loss. The hardware accelerator, implementing the compressed model and the LS estimator on FPGA platforms using High-level Synthesis (HLS), features a fine-grained pipeline architecture and optimized dataflow strategies. Tested on a Zynq UltraScale+ RFSoC, the accelerator achieves sub-millisecond latency, providing up to 24x speed-up and over 33x improvement in energy efficiency compared to GPU-based solutions. Extensive evaluations demonstrate that the proposed design generalizes not only across various noise levels and user mobilities, but also to a variety of unseen channel profiles, outperforming state-of-the-art baselines. By unifying algorithmic innovation with hardware-aware design, our work presents a future-proof channel estimation solution for 5G MIMO systems.

cs.IT↗

arXiv2025

HHEML: Hybrid Homomorphic Encryption for Privacy-Preserving Machine Learning on Edge

Privacy-preserving machine learning (PPML) is an emerging topic to handle secure machine learning inference over sensitive data in untrusted environments. Fully homomorphic encryption (FHE) enables computation directly on encrypted data on the server side, making it a promising approach for PPML. However, it introduces significant communication and computation overhead on the client side, making it impractical for edge devices. Hybrid homomorphic encryption (HHE) addresses this limitation by combining symmetric encryption (SE) with FHE to reduce the computational cost on the client side, and combining with an FHE-friendly SE can also lessen the processing overhead on the server side, making it a more balanced and efficient alternative. Our work proposes a hardware-accelerated HHE architecture built around a lightweight symmetric cipher optimized for FHE compatibility and implemented as a dedicated hardware accelerator. To the best of our knowledge, this is the first design to integrate an end-to-end HHE framework with hardware acceleration. Beyond this, we also present several microarchitectural optimizations to achieve higher performance and energy efficiency. The proposed work is integrated into a full PPML pipeline, enabling secure inference with significantly lower latency and power consumption than software implementations. Our contributions validate the feasibility of low-power, hardware- accelerated HHE for edge deployment and provide a hardware- software co-design methodology for building scalable, secure machine learning systems in resource-constrained environments. Experiments on a PYNQ-Z2 platform with the MNIST dataset show over a 50x reduction in client-side encryption latency and nearly a 2x gain in hardware throughput compared to existing FPGA-based HHE accelerators.

cs.CR↗

arXiv2023

Singularity formation along the line bundle mean curvature flow

The line bundle mean curvature flow is a complex analogue of the mean curvature flow for Lagrangian graphs, with fixed points solving the deformed Hermitian-Yang-Mills equation. In this paper we construct two distinct examples of singularities along the flow. First, we find a finite time singularity, ruling out long time existence of the flow in general. Next we show long time existence of the flow with a Calabi symmetry assumption on the blowup of $\mathbb P^n$, $n\geq 3$, if one assumes supercritical phase. Using this, we find an example where a singularity occurs at infinite time along the destabilizing subvariety in the semi-stable case.

math.DG↗

arXiv2019

Borsuk-Ulam theorems for products of spheres and Stiefel manifolds revisited

We give a different and possibly more accessible proof of a general Borsuk--Ulam theorem for a product of spheres, originally due to Ramos. That is, we show the non-existence of certain $(\mathbb{Z}/2)^k$-equivariant maps from a product of $k$ spheres to the unit sphere in a real $(\mathbb{Z}/2)^k$-representation of the same dimension. Our proof method allows us to derive Borsuk--Ulam theorems for certain equivariant maps from Stiefel manifolds, from the corresponding results about products of spheres, leading to alternative proofs and extensions of some results of Fadell and Husseini.

math.AT↗