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Approximate Bitcoin Mining

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Approximate Bitcoin Mining ( approximate-bitcoin-mining )

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Matthew Vilim University of Illinois at Urbana-Champaign mvilim2@illinois.edu ABSTRACT Bitcoin is the most popular cryptocurrency today. A bedrock of the Bitcoin framework is mining, a computation intensive process that is used to verify Bitcoin transactions for profit. We observe that mining is inherently error tolerant due to its embarrassingly parallel and probabilistic nature. We exploit this inherent tolerance to inaccuracy by proposing approxi- mate mining circuits that trade off reliability with area and delay. These circuits can then be operated at Better Than Worst-Case (BTWC) to enable further gains. Our results show that approximation has the potential to increase min- ing profits by 30%. CCS Concepts •Hardware → Fault tolerance; Keywords Bitcoin; SHA-256; Approximate Computing; Error-Tolerance 1. INTRODUCTION The Bitcoin cryptocurrency provides a decentralized and distributed method of verifying monetary transactions be- tween trustless parties1. Although cryptocurrencies had been proposed previously, Bitcoin was the first to provide a truly trustless solution. Unlike a traditional monetary system which is issued and backed by a single entity, Bitcoin requires no central administrator nor trust between participants. Traditionally, the difficulty in creating a distributed cur- rency is the need for a scheme to prevent double spending. One party might simultaneously broadcast two transactions, sending the same coins to two separate parties on the net- work; but without a central server to arbitrate both transac- tions and decide which is valid, disagreement arises over the true history and ownership of a given coin. Created in 2008, 1At the time of this writing, Bitcoin’s market capitalization is $5.5 billion USD. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full cita- tion on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or re- publish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. DAC ’16, June 05-09, 2016, Austin, TX, USA ⃝c 2016ACM.ISBN978-1-4503-4236-0/16/06...$15.00 DOI: http://dx.doi.org/10.1145/2897937.2897988 Approximate Bitcoin Mining Henry Duwe University of Illinois at Urbana-Champaign duweiii2@illinois.edu Rakesh Kumar University of Illinois at Urbana-Champaign rakeshk@illinois.edu Bitcoin resolves this problem and guarantees consensus of ownership by maintaining a public ledger (Section 2.1) of all transactions, called the blockchain [11]. New transactions are grouped together and are checked against the existing history to ensure all new transactions are valid. Bitcoin’s authenticity is assured by those who contribute computation power to its network (known as miners) to ver- ify and append transactions to a public ledger. Miners’ will- ingness to lend their computation power to the network, typ- ically in the form of ASICs dedicated to mining, in exchange for reward (profit) is critical to the security and survival of Bitcoin. In this paper, we observe that Bitcoin mining is a suitable candidate for approximate computing. As we demonstrate, Bitcoin mining is intrinsically resilient to errors; its parallel nature minimizes the propagation of errors incurred while searching for a solution, and Bitcoin’s distributed verifica- tion system detects and invalidates any potentially erroneous solutions. As such, a Bitcoin mining ASIC can be built out of approximate circuits that trade off circuits’ reliability for reduced delay and area; an appropriate approximate circuit will maximize profit even when producing results that are not guaranteed to be correct. We propose two forms of approximation. Functional ap- proximation is performed by replacing circuits with approx- imate versions to reduce area or delay. The reclaimed tim- ing slack may then be used to raise frequency and increase throughput. Operational approximation is performed by re- ducing guard bands and running the circuit with negative timing slack (i.e. at an even higher frequency), allowing occa- sional timing failures and Better Than Worst-Case (BTWC) operation. Our results show a 30% increase in mining profit from these approximation techniques. 2. BITCOIN MINING 2.1 Overview To maintain the validity of transactions in the Bitcoin network, there must be an incentive to contribute to verify- ing transactions within the blockchain. Bitcoin provides this incentive by rewarding miners who contribute with new bit- coins for every block created. Without miners, new transac- tions cannot be added to the public ledger, and Bitcoin will not function. The mining process is summarized in Figure 1. Mining consists of searching for a cryptographic nonce value within a block such that the hash of the block falls within a certain range. The network scales the range to maintain an average rate of one new block every ten minutes.

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