Related papers: How to Securely Prune Bitcoin's Blockchain

The popularization of blockchains leads to a resurgence of interest in Byzantine Fault-Tolerant (BFT) state machine replication protocols. However, much of the work on this topic focuses on the underlying consensus protocols, with emphasis…

In the area of blockchain, numerous methods have been proposed for suppressing intentional forks by attackers more effectively than the random rule. However, all of them, except for the random rule, require major updates, rely on a trusted…

Due to the open-source nature of the blockchain ecosystem, it is common for new blockchains to fork or partially reuse the code of classic blockchains. For example, the popular Dogecoin, Litecoin, Binance BSC, and Polygon are all variants…

As the need for more accurate and powerful Convolutional Neural Networks (CNNs) increases, so too does the size, execution time, memory footprint, and power consumption. To overcome this, solutions such as pruning have been proposed with…

The success of convolutional neural networks (CNNs) in various applications is accompanied by a significant increase in computation and parameter storage costs. Recent efforts to reduce these overheads involve pruning and compressing the…

Efficient data selection is crucial for enhancing the training efficiency of deep neural networks and minimizing annotation requirements. Traditional methods often face high computational costs, limiting their scalability and practical use.…

Public blockchains are decentralized networks where each participating node executes the same decision-making process. This form of decentralization does not scale well because the same data are stored on each network node, and because all…

The deployment of Convolutional Neural Networks (CNNs) on resource constrained platforms such as mobile devices and embedded systems has been greatly hindered by their high implementation cost, and thus motivated a lot research interest in…

Network pruning is an important research field aiming at reducing computational costs of neural networks. Conventional approaches follow a fixed paradigm which first trains a large and redundant network, and then determines which units…

Blockchain is currently one of the fastest-growing technologies in the field of Computer Science. It has found a prevalent use in financial applications like cryptocurrency, for example, Bitcoin and Ethereum. They have been able to bring an…

Deep neural networks (DNNs) are nowadays witnessing a major success in solving many pattern recognition tasks including skeleton-based classification. The deployment of DNNs on edge-devices, endowed with limited time and memory resources,…

In deep learning frameworks, weight pruning is a widely used technique for improving computational efficiency by reducing the size of large models. This is especially critical for convolutional operators, which often act as performance…

Blockchain and blockchain-inspired decentralized applications are on the rise thanks to their unique characteristics such as their decentralized nature, anonymity, and tamper-proof nature; however, blockchain transactions tend to experience…

Network compression is crucial to making the deep networks to be more efficient, faster, and generalizable to low-end hardware. Current network compression methods have two open problems: first, there lacks a theoretical framework to…

We present a provable, sampling-based approach for generating compact Convolutional Neural Networks (CNNs) by identifying and removing redundant filters from an over-parameterized network. Our algorithm uses a small batch of input data…

Bitcoin, Ethereum and other blockchain-based cryptocurrencies, as deployed today, cannot scale for wide-spread use. A leading approach for cryptocurrency scaling is a smart contract mechanism called a payment channel which enables two…

Blockchain, as a distributed ledger technology, becomes increasingly popular, especially for enabling valuable cryptocurrencies and smart contracts. However, the blockchain software systems inevitably have many bugs. Although bugs in smart…

The proliferation of resource-constrained devices has become prevalent across various digital applications, including smart homes, smart healthcare, and smart transportation, among others. However, the integration of these devices brings…

This paper reviews and highlights how coding schemes have been used to solve various problems in blockchain systems. Specifically, these problems relate to scaling blockchains in terms of their data storage, computation and communication…

In recent years, deep neural networks have known a wide success in various application domains. However, they require important computational and memory resources, which severely hinders their deployment, notably on mobile devices or for…