Re-Tangle: A ReRAM-based Processing-in-Memory Architecture for Transaction-based Blockchain

Abstract

Blockchain has gained a lot of attentions from both academic and industry. Transaction-based approaches such like Tangle structure, which is based on a DAG (Directed Acyclic Graph), are emerging to solve the blockchain scalability issues for IoT environment. In transaction-based blockchain, for a transaction, namely a node, to be attached to the Tangle, it needs to verify two other transactions. However, with the Tangle expanding, this attaching process consumes huge computational resources and energy, which severely limits the performance of the transaction-based blockchain. In this paper, we present Re-Tangle, a novel transaction-based blockchain acceleration architecture that explores the opportunity of performing massive parallel operations with low hardware and energy cost. Re-Tangle consists of a random walking module and a transaction validation module, which transfer Tangle functions into ReRAM-based logic analog computation units. In the random walking module, Re-Tangle maintains a exponentiation translator to reduce its design complexity and improve its computation efficiency for exponentiation. In the transaction validation module, Re-Tangle further proposes a highly parallel modular unit to accelerate the validation of different tags in a transaction. The experience results show that Re-Tangle distinguishes itself from other architectures, with significant performance improvement and energy saving. The throughput of Re-Tangle is about 19.4× and 2.13× higher compared with CPU and GPU, respectively, and the energy consumption of Re-Tangle is 63.35 × and 4.92 × less.