Taipan: a block-chain with democratic consensus and validated contracts. Blockchains keep records by mass collaboration using peer-to-peer and cryptographical algorithms. Programmable blockchain technology can disrupt the finance industry, governance, and legal services by reducing the role for intermediaries such as banks and government authorities. This project aims to propose a new block-chain for “Trust Among Individual ParticipANts” (TAIPAN). The main feature of TAIPAN’s programmable block ....Taipan: a block-chain with democratic consensus and validated contracts. Blockchains keep records by mass collaboration using peer-to-peer and cryptographical algorithms. Programmable blockchain technology can disrupt the finance industry, governance, and legal services by reducing the role for intermediaries such as banks and government authorities. This project aims to propose a new block-chain for “Trust Among Individual ParticipANts” (TAIPAN). The main feature of TAIPAN’s programmable block-chain is the integrity and security of individual ownership records that current block-chains lack. This project will aim to overcome two major threats in current programmable block-chains, double-spending among participants, and security vulnerabilities in smart contracts. TAIPAN will provide a democratic and leaderless consensus algorithm that will avoid double-spending, and a new bug-checking framework for smart contracts that finds anomalies before smart contracts are admitted to the block-chain.Read moreRead less
Edge-Accelerated Deep Learning. Implementing deep learning (DL) applications usually requires a large amount of collected data and powerful computing resources in the cloud. However, this centralised approach has issues of high latency, large bandwidth usage, and possible privacy violation for many practical applications. Without properly addressing these issues, the wider application of DL in practice will seriously be hindered. This project aims to solve several key challenging problems in eff ....Edge-Accelerated Deep Learning. Implementing deep learning (DL) applications usually requires a large amount of collected data and powerful computing resources in the cloud. However, this centralised approach has issues of high latency, large bandwidth usage, and possible privacy violation for many practical applications. Without properly addressing these issues, the wider application of DL in practice will seriously be hindered. This project aims to solve several key challenging problems in effective deployment and efficient execution of DL applications in a distributed edge-computing environment. Several innovative edge-computing methods will be developed for DL training, inference and implementation to achieve high performance with low latency and enhanced privacy.Read moreRead less