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Delta compression can complement data deduplication by further minimizing redundancy through the compression of non-duplicate data chunks. When adding delta compression to deduplication-based backup systems, however, two primary challenges arise that ...

Music’s profound impact on emotions and mood has prompted growing interest in developing AI models capable of generating music that aligns with specific emotional intents. However, existing methods, such as those utilizing modified LSTM networks or GANs, ...

Positive-unlabeled (PU) learning deals with binary classification problems where only positive and unlabeled data are available. In this paper, we introduce a novel boosting framework, Adaptive PU (AdaPU), for learning from PU data. AdaPU builds ...

Gastric intestinal metaplasia (GIM) is a frequent lesion of the gastrointestinal tract, and its clinical grading can significantly reduce the risk of gastric cancer. Pre-trained vision–language models have demonstrated impressive generalizability ...

Graph processing is a vital component of many AI and big data applications. However, due to its poor locality and complex data access patterns, graph processing is also a known performance killer of AI and big data applications. In this work, we ...

The use of cross-scheme fully homomorphic encryption (FHE) in privacy-preserving applications present to be a new challenge to hardware accelerator design. Existing accelerator architectures with customized polynomial-level operator abstraction fail to ...

With the rapid development of artificial intelligence, automated endoscopy-assisted diagnostic systems have become an effective tool for reducing the diagnostic costs and shortening the treatment cycle of patients. Typically, the performance of ...

• The proposed STE-StyleGAN model implements image-to-image compilation and generates high-resolution endoscopic images.
• Swin transformer hierarchically encodes images and self-attention strengthens the connections between different ...

This paper aims at high and portable performance for tensor computations across spatial (e.g., FPGAs) and vector architectures (e.g., GPUs). The state-of-the-art usually address performance portability across vector architectures (CPUs and GPUs). However,...

Text classification is a widely used task in natural language processing. However, the presence of stereotype bias in text classification can lead to unfair and inaccurate predictions. Stereotype bias is particularly prevalent in words that are ...

Language expressions without empathy can neither effectively convey the expresser’s concern and goodwill, but also have a negative effect on the emotional and mental health of the recipients of the information. Compared to harsh or aggressive ...

Along with the rapid evolution of deep neural networks, the ever-increasing complexity imposes formidable computation intensity to the hardware accelerator. In this paper, we propose a novel computing philosophy called “bit interleaving” and the ...

Time series chains are a set of subsequence patterns arranged in chronological order, such that each pattern is similar to the one before it, but the first and last patterns can be arbitrarily different. At present, time series chain discovery ...

Classic DNN pruning mostly leverages software-based methodologies to tackle the accuracy/speed tradeoff, which involves complicated procedures like critical parameter searching, fine-tuning and sparse training to find the best plan. In this paper, we ...

Processing-in-Memory (PIM) is considered a promising solution to improve the performance of graph-computing applications by minimizing the data movement between the host and memory. Which workload to offload and how to offload it to PIM logic determine ...

Genomics is the critical key to enabling precision medicine, ensuring global food security and enforcing wildlife conservation. The massive genomic data produced by various genome sequencing technologies presents a significant challenge for genome ...

MLC PCM provides high-density data storage and extended data retention; therefore it is a promising alternative for DRAM main memory. However, its low write performance is a major obstacle to commercialization. One opportunity for improving the latency of ...

3D vertical ReRAM (3DV-ReRAM) emerges as one of the most promising alternatives to DRAM due to its good scalability beyond 10nm. Monolithic 3D (M3D) integration enables 3DV-ReRAM to improve its array area efficiency by stacking peripheral circuits ...

While CPU architectures are incorporating many more cores to meet ever-bigger workloads, advance in fault-tolerance support is indispensable for sustaining system performance under reliability constraints. Emerging non-volatile memory technologies are ...

Scalable locking is a key building block for scalable multi-threaded software. Its performance is especially critical in multi-socket, multi-core machines with non-uniform memory access (NUMA). Previous schemes such as in-place locks and delegation ...

Scalable locking is a key building block for scalable multi-threaded software. Its performance is especially critical in multi-socket, multi-core machines with non-uniform memory access (NUMA). Previous schemes such as local locking and remote locking ...