A Standalone FPGA-based Miner for Lyra2REv2 Cryptocurrencies

Lyra2REv2 is a hashing algorithm that consists of a chain of individual hashing algorithms, and it is used as a proof-of-work function in several cryptocurrencies. The most crucial and exotic hashing algorithm in the Lyra2REv2 chain is a specific instance of the general Lyra2 algorithm. This work presents the first hardware implementation of the specific instance of Lyra2 that is used in Lyra2REv2. Several properties of the aforementioned algorithm are exploited in order to optimize the design. In addition, an FPGA-based hardware implementation of a standalone miner for Lyra2REv2 on a Xilinx Multi-Processor System on Chip is presented. The proposed Lyra2REv2 miner is shown to be significantly more energy efficient than both a GPU and a commercially available FPGA-based miner. Finally, we also explain how the simplified Lyra2 and Lyra2REv2 architectures can be modified with minimal effort to also support the recent Lyra2REv3 chained hashing algorithm.Comment: 13 pages, accepted for publication in IEEE Trans. Circuits Syst. I. arXiv admin note: substantial text overlap with arXiv:1807.0576

Mining CryptoNight-Haven on the Varium C1100 Blockchain Accelerator Card

Cryptocurrency mining is an energy-intensive process that presents a prime candidate for hardware acceleration. This work-in-progress presents the first coprocessor design for the ASIC-resistant CryptoNight-Haven Proof of Work (PoW) algorithm. We construct our hardware accelerator as a Xilinx Run Time (XRT) RTL kernel targeting the Xilinx Varium C1100 Blockchain Accelerator Card. The design employs deeply pipelined computation and High Bandwidth Memory (HBM) for the underlying scratchpad data. We aim to compare our accelerator to existing CPU and GPU miners to show increased throughput and energy efficiency of its hash computation

Crypto-Currencies: does sentiment play a role?

This Master Thesis addresses the relationship between Market/ Investor Sentiment and the Crypto-Currencies Market using a database with 28 variables, such as the Crypto-Currencies Prices (Crypto-Currencies that had more than one Billion USD in Market Cap were selected), the S&P500 Index stock prices, the GDP of the US or Europe, Internet World Statistical data, among others, but most importantly Investor/ Market Sentiment data, gathered from Duke’s University surveys. All this data was then entered into SPSS and analyzed as Panel Data. The time period for this research spans from 2013 to 2019, 2013 because it is the first year were the market prices of Crypto-Currencies are available. Even though Bitcoin was created in 2008 (Nakamoto, 2008), market data about Crypto-Currencies only appeared in 2013. We run OLS and 2SLS regressions to test the significance, causality and the relationship between Sentiment and Crypto-Currencies. For the 2SLS, two Lagged variables were added. Additional Robustness tests were done including regressions with only Bitcoin and Litecoin. With this we find that there is a strong correlation and significance between the level of optimism and/ or pessimism in the financial markets and in Crypto-Currencies and that the relationship between them is mostly non-linear according to our research and analysis. Our results suggest that the price of Crypto-Currencies is an increasing function of sentiment, which calls for considering Behavioral Economics tenets when analyzing Crypto-Currencies markets’. Our findings are confirmed by robustness tests deploying alternative measures of the variable of interest – Sentiment – as well as alternative controls. The findings suggest that more regulation in the market is needed to avoid extreme volatility for investors. Crypto-Currencies show bigger growth when the sentiment towards the market is pessimistic, such as when there is a crisis either a monetary or a political one, because Crypto-Currencies are still associated with somewhat non-deterministic movements. It is now our job to pursue regulation and to attract more corporate investors so that Crypto-Currencies could start to be more accepted as financial assets, and not to be seen as just “internet money”, as unfortunately they are still perceived by many nowadays

A Standalone FPGA-Based Miner for Lyra2REv2 Cryptocurrencies

Lyra2REv2 is a hashing algorithm that consists of a chain of individual hashing algorithms, and it is used as a proof-of-work function in several cryptocurrencies. The most crucial and exotic hashing algorithm in the Lyra2REv2 chain is a specific instance of the general Lyra2 algorithm. This work presents the first hardware implementation of the specific instance of Lyra2 that is used in Lyra2REv2. Several properties of the aforementioned algorithm are exploited in order to optimize the design. In addition, an FPGA-based hardware implementation of a standalone miner for Lyra2REv2 on a Xilinx Multi-Processor System on Chip is presented. The proposed Lyra2REv2 miner is shown to be significantly more energy efficient than both a GPU and a commercially available FPGA-based miner. Finally, we also explain how the simplified Lyra2 and Lyra2REv2 architectures can be modified with minimal effort to also support the recent Lyra2REv3 chained hashing algorithm

A Standalone FPGA-Based Miner for Lyra2REv2 Cryptocurrencies

\u3cp\u3eLyra2REv2 is a hashing algorithm that consists of a chain of individual hashing algorithms, and it is used as a proof-of-work function in several cryptocurrencies. The most crucial and exotic hashing algorithm in the Lyra2REv2 chain is a specific instance of the general Lyra2 algorithm. This work presents the first hardware implementation of the specific instance of Lyra2 that is used in Lyra2REv2. Several properties of the aforementioned algorithm are exploited in order to optimize the design. In addition, an FPGA-based hardware implementation of a standalone miner for Lyra2REv2 on a Xilinx Multi-Processor System on Chip is presented. The proposed Lyra2REv2 miner is shown to be significantly more energy efficient than both a GPU and a commercially available FPGA-based miner. Finally, we also explain how the simplified Lyra2 and Lyra2REv2 architectures can be modified with minimal effort to also support the recent Lyra2REv3 chained hashing algorithm.\u3c/p\u3