18 papers · ranked by Valyu relevance
Feng Cheng, Cong Guo, Chiyue Wei, Junyao Zhang + 6 more
'Edward Hanson' 'Jiaqi Zhang' 'Xiaoxiao Liu' 'Hai "Helen" Li' 'Yiran Chen'] Large language models (LLMs) have demonstrated transformative capabilities across diverse artificial intelligence applications, yet their deployment is hindered by substantial memory and computational demands, especially in resource-constrained…
Sandeep A. Kumar, Aravinda Prasad, Sreenivas Subramoney
As opposed to the state-of-the-art solutions that employ a 2-Tier solution, a single compressed tier along with DRAM, we define multiple compressed tiers implemented through a combination of different compression algorithms, memory allocators for compressed objects, and backing media to store compressed objects. These…
Kailun Jin, Yajuan Du, Mingzhe Zhang, Zhenghao Yin + 2 more
'Rachata Ausavarungnirun' 'Aiqun Liu'] Due to its non-volatility and large capacity, NVM devices gradually take place at various levels of memories. However, their limited endurance is still a big concern for large-scale data centres. Compression algorithms have been used to save NVM space and enhance the efficiency of…
Adeyemi Aina
C/C++ on a Broader Range of Workloads Authors: ['Adeyemi Aina'] Memory compression is an important approach in computer architecture for decreasing memory footprint and improving system performance. In this paper, we use C/C++ to develop a current memory compression algorithm; the Global Bases Delta Immediate (GBDI)…
Angelos Arelakis, Nilesh Shah, Yiannis Nikolakopoulos, Dimitrios Palyvos-Giannas
'Dimitrios Palyvos-Giannas'] In our exploration of Composable Memory systems utilizing CXL, we focus on overcoming adoption barriers at Hyperscale, underscored by economic models demonstrating Total Cost of Ownership (TCO). While CXL addresses the pressing memory capacity needs of emerging Hyperscale applications, the…
Dale Zhou, Sharon M. Noh, Nora C. Harhen, Nidhi V. Banavar + 3 more
The ability to discriminate similar visual stimuli has been used as an important index of memory function. This ability is widely thought to be supported by expanding the dimensionality of relevant neural codes, such that neural representations for the similar stimuli are maximally distinct, or “separated.” An…
Yu Liang, Aofeng Shen, Chun Jason Xue, Riwei Pan + 8 more
Fast Application Relaunch and Reduced CPU Usage on Mobile Devices Authors: ['Yu Liang' 'Aofeng Shen' 'Chun Jason Xue' 'Riwei Pan' 'Haiyu Mao' 'Nika Mansouri Ghiasi' 'Qi–Chuan Jiang' 'Rakesh Nadig' 'Li Lei' 'Rachata Ausavarungnirun' 'Mohammad Sadrosadati' 'Onur Cezmi Mutlu'] As the memory demands of individual mobile…
Yulin Feng, Yizhou Zhang, Zheng Zhou, Peng Huang + 3 more
'Xiaoyan Liu' 'Jinfeng Kang'] The exponential growth of various complex images is putting tremendous pressure on storage systems. Here, we propose a memristor-based storage system with an integrated near-storage in-memory computing-based convolutional autoencoder compression network to boost the energy efficiency and…
Tshimankinda Jerome Ngoy, Mike Nkongolo
| Embedded Systems- Digital | ABSTRACT | | --- | --- | | Signal Processing- Energy | | | Generation | | | Article history: | The purpose of this project is to determine a method for compressing the | | | function codes located in the non-volatile memory of the on-board system | | Preprint version: 19 July 2023 | after…
Runzhao Yang, Tingxiong Xiao, Yuxiao Cheng, Anan Li + 7 more
Efficient storage and sharing of massive biomedical data would open up their wide accessibility to different institutions and disciplines. However, compressors tailored for natural photos/videos are rapidly limited for biomedical data, while emerging deep learning based methods demand huge training data and are…
Puguang Liu, Ziling Wei, Chuan Yu, Shuhui Chen + 1 more
Lossless data compression is a crucial and computing-intensive application in data-centric scenarios. To reduce the CPU overhead, FPGA-based accelerators have been proposed to offload compression workloads. However, most existing schemes have the problem of an imbalanced resource utilization and a poor practicability.…
Yicong Li, Core Francisco Park, Daniel Xenes, Caitlyn Bishop + 7 more
The ongoing pursuit to map detailed brain structures at high resolution using electron microscopy (EM) has led to advancements in imaging that enable the generation of connectomic volumes that have reached the petabyte scale and are soon expected to reach the exascale for whole mouse brain collections. To tackle the…
M Baritha Begum, N. Deepa, Mueen Uddin, Rajesh Kaluri + 2 more
'Maha Abdelhaq' 'Raed Alsaqour'] Data stored on physical storage devices and transmitted over communication channels often have a lot of redundant information, which can be reduced through compression techniques to conserve space and reduce the time it takes to transmit the data. The need for adequate security…
Miaoshan Lu, Junjie Tong, Ruimin Wang, Shaowei An + 2 more
Mass spectrum (MS) data volumes increase with an improved ion acquisition ratio and a highly accurate mass spectrometer. However, the most widely used data format, mzML, does not take advantage of compression methods and improved read performances. Several compression algorithms have been proposed in recent years, and…
David Podgorelec, Damjan Strnad, Ivana Kolingerová, Borut Žalik + 1 more
'Jun Chen'] After a boom that coincided with the advent of the internet, digital cameras, digital video and audio storage and playback devices, the research on data compression has rested on its laurels for a quarter of a century. Domain-dependent lossy algorithms of the time, such as JPEG, AVC, MP3 and others…
Vlad-Ilie Ungureanu, Paul Negirla, Adrian Korodi, Attilio Di Nisio + 1 more
'Gwanggil Jeon'] Image compression is a vital component for domains in which the computational resources are usually scarce such as automotive or telemedicine fields. Also, when discussing real-time systems, the large amount of data that must flow through the system can represent a bottleneck. Therefore, the storage of…
Yifan Ma, Chengqiang Yi, Yao Zhou, Zhaofei Wang + 9 more
With the rapid development in advanced imaging techniques, massive image data have been acquired for various biomedical applications, posing significant challenges to their efficient storage, transmission, and sharing. Classical model-or learning-based compression algorithms are optimized for specific dimensional data…
Daniel Probst
Last year, a preprint gained notoriety, proposing that a k-nearest neighbour classifier is able to outperform large-language models using compressed text as input and normalised compression distance (NCD) as a metric. In chemistry and biochemistry, molecules are often represented as strings, such as SMILES for small…