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Homomorphic encryption (HE) allows computations to be directly carried out on ciphertexts and is essential to privacy-preserving computing, such as neural network inference, medical diagnosis, and financial data analysis. Only addition and…

Magneto-Electric FET (MEFET) is a recently developed post-CMOS FET, which offers intriguing characteristics for high speed and low-power design in both logic and memory applications. In this paper, for the first time, we propose a…

The time domain inter-cell interference coordination techniques specified in LTE Rel. 10 standard improves the throughput of picocell-edge users by protecting them from macrocell interference. On the other hand, it also degrades the…

Alghoritms: HIEROCRYPT-3, CAMELLIA and ANUBIS, GRAND CRU, NOEKEON, NUSH, Q, RC6, SAFER++128, SC2000, SHACAL were requested for the submission of block ciphers (high level block cipher) to NESSIE (New European Schemes for Signatures,…

We address the challenge of incorporating encoded quantum memories into an exact secret key rate analysis for small and intermediate-scale quantum repeaters. To this end, we introduce the check matrix model and quantify the resilience of…

Token-level sparse attention mechanisms, exemplified by DeepSeek Sparse Attention (DSA), achieve fine-grained key selection by scoring every historical key for each query through a lightweight indexer, then computing attention only on the…

Superconductor electronics (SCE) is a promising complementary and beyond CMOS technology. However, despite its practical benefits, the realization of SCE logic faces a significant challenge due to the absence of dense and scalable…

To address the 'memory wall' problem in NN hardware acceleration, we introduce HALO-CAT, a software-hardware co-design optimized for Hidden Neural Network (HNN) processing. HALO-CAT integrates Layer-Penetrative Tiling (LPT) for algorithmic…

Modern heterogeneous computing architectures, which couple multi-core CPUs with discrete many-core GPUs (or other specialized hardware accelerators), enable unprecedented peak performance and energy efficiency levels. Unfortunately, though,…

Ensemble learning remains a cornerstone of machine learning, with stacking used to integrate predictions from multiple base learners through a meta-model. However, deep stacking remains uncommon due to feature redundancy, complexity, and…

We report on scaling and timing tests of the SIESTA electronic structure code for ab initio molecular dynamics simulations using density-functional theory. The tests are performed on six large-scale supercomputers belonging to the PRACE…

State-of-the-art sparse attention methods for reducing decoding latency fall into two main categories: approximate top-$k$ (and its extension, top-$p$) and recently introduced sampling-based estimation. However, these approaches are…

As the demand for privacy-preserving computation continues to grow, fully homomorphic encryption (FHE)-which enables continuous computation on encrypted data-has become a critical solution. However, its adoption is hindered by significant…

The migration to post-quantum cryptography is urgent for Internet of Things devices with 10--20 year lifespans, yet no systematic benchmarks exist for the finalised NIST standards on the most constrained 32-bit processor class. This paper…

Deep learning is a kind of feature learning method with strong nonliear feature transformation and becomes more and more important in many fields of artificial intelligence. Deep autoencoder is one representative method of the deep learning…

This research explores the use of superconductor electronics (SCE) for accelerating fully homomorphic encryption (FHE), focusing on the Number-Theoretic Transform (NTT), a key computational bottleneck in FHE schemes. We present SCE-NTT, a…

Binary quantization represents the most extreme form of compression, reducing weights to +/-1 for maximal memory and computational efficiency. While recent sparsity-aware binarization achieves sub-1-bit compression via weight pruning, it…

There is an urgent demand for privacy-preserving techniques capable of supporting compute and data intensive (CDI) computing in the era of big data. However, none of existing TEEs can truly support CDI computing tasks, as CDI requires high…

Attention accounts for an increasingly dominant fraction of total computation during inference for mixture-of-experts (MoE) models, making efficient acceleration critical. Emerging domain-specific accelerators for large model inference are…

Privacy-preserving solutions enable companies to offload confidential data to third-party services while fulfilling their government regulations. To accomplish this, they leverage various cryptographic techniques such as Homomorphic…