NFT Marketplace

In an increasingly digitized world, the secure management and trade of digital assets have become a pressing issue. This project aims to address this challenge by developing a decentralized application (dApp) that leverages blockchain technology and deep learning models to provide secure and efficient digital asset management, with a focus on NFTs. The dApp includes features such as secure wallet connections, NFT image generation, minting, marketplace, and profile management. The back-end of the dApp is implemented using the Goerli testnet with Solidity-based smart contracts, while IPFS and ReactJS/EtherJS are used for decentralized storage and front-end development, respectively. Additionally, the OpenAI API is integrated to generate unique NFT images based on user input. The project demonstrates the practical application of blockchain technology and deep learning models in developing dApps for secure and decentralized digital asset management. Overall, the project contributes to the ongoing research on blockchain-based solutions for secure digital asset management, while highlighting the potential of blockchain and deep learning technologies to transform the way we manage and trade digital assets.Comment: Report for MULTIMEDIA COMMUNICATIONS course projec

Manticore: Hardware-Accelerated RTL Simulation with Static Bulk-Synchronous Parallelism

The demise of Moore's Law and Dennard Scaling has revived interest in specialized computer architectures and accelerators. Verification and testing of this hardware heavily uses cycle-accurate simulation of register-transfer-level (RTL) designs. The best software RTL simulators can simulate designs at 1--1000~kHz, i.e., more than three orders of magnitude slower than hardware. Faster simulation can increase productivity by speeding design iterations and permitting more exhaustive exploration. One possibility is to use parallelism as RTL exposes considerable fine-grain concurrency. However, state-of-the-art RTL simulators generally perform best when single-threaded since modern processors cannot effectively exploit fine-grain parallelism. This work presents Manticore: a parallel computer designed to accelerate RTL simulation. Manticore uses a static bulk-synchronous parallel (BSP) execution model to eliminate runtime synchronization barriers among many simple processors. Manticore relies entirely on its compiler to schedule resources and communication. Because RTL code is practically free of long divergent execution paths, static scheduling is feasible. Communication and synchronization no longer incur runtime overhead, enabling efficient fine-grain parallelism. Moreover, static scheduling dramatically simplifies the physical implementation, significantly increasing the potential parallelism on a chip. Our 225-core FPGA prototype running at 475 MHz outperforms a state-of-the-art RTL simulator on an Intel Xeon processor running at $\approx$ 3.3 GHz by up to 27.9$\times$ (geomean 5.3$\times$) in nine Verilog benchmarks