Papers for
cryptocurrency infrastructure teams
Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.
Ethereum energy use drops sharply with new measurement method
Quantifying Ethereum Energy Consumption via Network Mapping
Abstract: Ethereum's electricity use fell by about 99.95% after the move from proof of work to proof of stake. Service providers still need to report operational energy use, e.g. under the EU Markets in Crypto-Assets Regulation (MiCAR). Existing estimates either apply one typical wattage to every node or start from aggregated monitoring counts. Both ignore attributes that nodes already advertise on the peer-to-peer network: client software, ARM or x86 hardware, hosting location, and validator role. We crawl the consensus and execution layers, assign each peer a wattage from those attributes using published measurements, and estimate the remaining incomplete peers with a Random Forest. On 6,934 peers from two Nebula crawls (19 and 22 June 2026), reachable nodes sum to 415 kW, or 3.63 GWh if that draw were held for a year. The same Lighthouse+Nethermind x86 wattage on every peer yields 431 kW. Observed attributes lower the total by 3.9%, mainly because nodes at Hetzner and other non-AWS clouds draw less than that home-desktop figure. AWS accounts for 15.6% of watts from 12.2% of peers, and validator-flagged nodes for 31.4% of watts from 25.7% of peers. The 415 kW snapshot is about 46% of the Cambridge Centre for Alternative Finance (CCAF) estimate of about 0.90 MW. Both use about 60 W per node, so the gap is mostly how many nodes each estimate includes. Rules cover 3,110 peers and the forest the other 3,824. On held-out labeled peers with client, architecture, and OS hidden, the forest's mean absolute error against the rule wattage is 4.3 W. Twenty-four-hour measurements on a gaming desktop differ from the predictions. After subtracting a 33 W idle graphics card that Ethereum clients do not need, both differences fall to about 19%.
FPGA accelerator speeds up secure proof computations for scalable blockchain
FASTAR: FRI Accelerator for Scalable Transparent ARguments of Knowledge
Abstract: Zero-Knowledge Proofs (ZKPs) enable a prover to cryptographically convince a verifier of the validity of a statement without revealing any underlying secrets, forming a foundational primitive for verifiable computation. The ZKP landscape is undergoing a fundamental shift from classic zk-SNARKs such as Groth16, which rely on trusted setup and are vulnerable to quantum adversaries, toward transparent, post-quantum constructions such as zk-STARK. These systems achieve post-quantum security by relying solely on collision-resistant hash functions, however, at the cost of substantial computational overhead. In particular, the Fast Reed--Solomon Interactive Oracle Proof of Proximity (FRI) protocol dominates prover complexity, generating massive data volumes, repeated Merkle-tree commitments, and irregular memory access patterns that limit performance and energy efficiency on general-purpose processors. To address these challenges, this work proposes FASTAR, a novel FPGA-based accelerator for the FRI protocol. Unlike accelerators that pursue fixed high-performance kernels on expensive ASIC process nodes, FASTAR adopts a constraint-driven design methodology. Our framework is implemented with High-Level Synthesis (HLS) and composed of fully parameterizable building blocks for the major stages of FRI, including polynomial evaluation, recursive split-and-fold, and Merkle-tree construction. From user-provided board specifications, FASTAR automatically generates hardware implementations tailored to the resource and memory constraints of the target FPGA, enabling deployment across a wide range of platforms without manual redesign.