Deploying smart contracts and invoking their functions on block-chains incur gas costs, which depend on the operations executed by those functions. This makes optimizing the gas cost of smart contract functions a rewarding goal. However, existing approaches to gas cost optimization of smart contracts mainly involve rule-based optimization or automatic optimization for specific types of patterns. In this paper, we discuss a novel approach to automatically retrieving optimized versions of Solidity functions from a repository of smart contracts. The system identifies and suggests gas-efficient alternatives that maintain functional equivalence by comparing the opcode sequences of individual functions. We evaluate this approach on a dataset of 16,529 functions from real-world contracts, demonstrating substantial gas savings, as high as 34% on average when considering the most similar functions.
Boyuan Feng, Zheng Wang, Yuke Wang, Shu Yang · 5 authors
Zero knowledge Neural Networks draw increasing attention for guaranteeing computation integrity and privacy of neural networks (NNs) based on zero-knowledge Succinct Non-interactive ARgument of Knowledge (zkSNARK) security scheme. However, the performance of zkSNARK NNs is far from optimal due to the million-scale circuit computation with heavy scalar-level dependency. In this paper, we propose a type-based optimizing framework for efficient zero-knowledge NN inference, namely ZENO (ZEro knowledge Neural network Optimizer). We first introduce ZENO language construct to maintain high-level semantics and the type information (e.g., privacy and tensor) for allowing more aggressive optimizations. We then propose privacy-type driven and tensor-type driven optimizations to further optimize the generated zkSNARK circuit. Finally, we design a set of NN-centric system optimizations to further accelerate zkSNARK NNs. Experimental results show that ZENO achieves up to 8.5× end-to-end speedup than state-of-the-art zkSNARK NNs. We reduce proof time for VGG16 from 6 minutes to 48 seconds, which makes zkSNARK NNs practical.
The performance bottleneck of blockchain has shifted from consensus to serial smart contract execution in transaction validation. Previous works predominantly focus on inter-contract parallel execution, but they fail to address the inherent limitations of each smart contract execution performance. In this paper, we propose PaVM, the first smart contract virtual machine that supports both inter-contract and intra-contract parallel execution to accelerate the validation process. PaVM consists of (1) key instructions for precisely recording entire runtime information at the instruction level, (2) a runtime system with a re-designed machine state and thread management to facilitate parallel execution, and (3) a read/write-operation-based receipt generation method to ensure both the correctness of operations and the consistency of blockchain data. We evaluate PaVM on the Ethereum testnet, demonstrating that it can outperform the mainstream blockchain client Geth. Our evaluation results reveal that PaVM speeds up overall validation performance by 33.4×, and enhances validation throughput by up to 46×.
Sidechains have been widely used to improve the interoperability and scalability of blockchain systems. Despite several interesting sidechain constructions have been proposed in the literature, they suffer from the following downsides: (1) their designs do not easily support pluggable consensus mechanisms, and (2) their communication and storage costs for cross-chain operations are not yet optimized. In this work, we first propose Ge-Co, a generic sidechain construction to realize secure asset transfers between blockchains, supporting different consensus algorithms, such as Proof-of-Stake (PoS) and Proof-of-Work (PoW). Our design is built on top of the proposed voting committee selection approach and threshold signature schemes (TSS) and meanwhile, it achieves optimally succinct and constant proof size, only yielding lightweight communication and storage costs. Ge-Co works in the semi-adaptive corruption model. To provide stronger security, we further propose PoS-Co, a PoS-based sidechain construction in the fully-adaptive corruption model. PoS-Co is based on the proposed anonymous committee selection approach, and preserves optimally succinct proof. We also formally prove that Ge-Co can achieve the security properties of atomicity and timeliness. Finally, we develop a proof-of-concept (PoC) implementation for Ge-Co, and the results demonstrate that the design is efficient and practical.
Estimates of energy usage in layers of computing from devices to algorithms have been determined and analyzed. Building on the previous analysis [3], energy needed from single devices and systems including three large-scale computing applications such as Artificial Intelligence (AI)/Machine Learning for Natural Language Processing, Scientific Simulations, and Cryptocurrency Mining have been estimated. In contrast to the bit-level switching, in which transistors achieved energy efficiency due to geometrical scaling, higher energy is expended both at the at the instructions and simulations levels of an application. Additionally, the analysis based on AI/ML Accelerators indicate that changes in architectures using an older semiconductor technology node have comparable energy efficiency with a different architecture using a newer technology. Further comparisons of the energy in computing systems with the thermodynamic and biological limits, indicate that there is a 27–36 orders of magnitude higher energy requirements for total simulation of an application. These energy estimates underscore the need for serious considerations of energy efficiency in computing by including energy as a design parameter, enabling growing needs of compute-intensive applications in a digital world.
Konstantinos Sgantzos, Mohamed Al Hemairy, Panagiotis Tzavaras, Spyridon Stelios
The usage of Large Language Models (LMMs) and their exponential progress has created a Cambrian Explosion in the development of new tools for almost every field of science and technology, but also presented significant concerns regarding the AI ethics and creation of sophisticated malware and phishing attacks. Moreover, several worries have arisen in the field of dataset collection and intellectual property in that many datasets may exist without the license of the respective owners. Triple-Entry Accounting (TEA) has been proposed by Ian Grigg to increase transparency, accountability, and security in financial transactions. This method expands upon the traditional double-entry accounting system, which records transactions as debits and credits in two separate ledgers, by incorporating a third ledger as an independent verifier via a digitally signed receipt. The utilization of a digital signature provides evidentiary power to the receipt, thus reducing the accounting problem to one of the presence or absence of the receipt. The integrity issues associated with double-entry accounting can be addressed by allowing the parties involved in the transaction to share the records with an external auditor. This manuscript proposes a novel methodology to apply triple-entry accounting records on a publicly accessed distributed ledger technology medium to control the queries of LLMs in order to discourage malicious acts and ensure intellectual property rights.
Concurrent execution of smart contracts is a promising approach to boost their performance in current blockchain systems. However, prior work on concurrent execution only considered each smart contract transaction as a single atomic unit for the concurrent scheduling, which is not suitable for the increasingly complex smart contracts with high contention in practice. In this article, we presentSC-Chef, a new concurrency control system to turboboost smart contract concurrent execution for high contention workloads via the transaction decomposition. The key novelty of ourSC-Chefis to decompose the original transaction into multiple subtransactions that can be executed independently, while still respecting the original logic dependency. We implement and evaluate ourSC-Chefin the private Ethereum platform. Our extensive experiments show that ourSC-Chefoutperforms the classic optimistic concurrency control and two-phase lock by 60% and 70% on average in the case of high contention, respectively.
With the advancement of Urban Rail Transits (URTs), the demand for artificial intelligence (AI) based URTs services grows exponentially. Edge intelligence (EI) leverages computing resources on the network edge to provide realtime intelligent services in close proximity. As it enables fast distributed learning, EI is envisioned to be a potential component of URTs, and ideal EI service provision is a critical concern for the intelligent development of URTs. The existing EI-related research concentrates on the computation offloading of general AI-based tasks, whereas both the edge server deployment and AI model training process are not explicitly designed for URTs. The URTs AI service characteristics such as model training demand, priority, and security are largely ignored. In this paper, we propose a novel collaborative EI service provision framework for URTs. Blockchain is used along with the EI server to construct a trusted computing infrastructure. To address the EI service credit crisis, a blockchain-based trust management mechanism including short-term reward incentives and long-term reputation evaluation is designed in the trusted computing infrastructure. An HRL-based collaborative training service optimization model is proposed to improve the learning efficiency and edge resource utilization rate in URTs. Specifically, the proposed two-stage collaborative optimization model jointly considers high-level service scheduling and low-level task offloading. In addition, we present an intelligent train control model based on the state-ofthe-art decision transformer (DT), with the training service as a case study to demonstrate the effectiveness of the proposed collaborative EI service provision. Extensive simulation results show that the proposed EI service provision framework can provide trusted, efficient, and high-quality AI training services, simultaneously improving URTs operational efficiency.
Ning Lin, Shaocong Wang, Yi Li, Bo Wang · 22 authors
The human brain is a complex spiking neural network (SNN), capable of learning multimodal signals in a zero-shot manner by generalizing existing knowledge. Remarkably, it maintains minimal power consumption through event-based signal propagation. However, replicating the human brain in neuromorphic hardware presents both hardware and software challenges. Hardware limitations, such as the slowdown of Moore's law and Von Neumann bottleneck, hinder the efficiency of digital computers. Additionally, SNNs are characterized by their software training complexities. To this end, we propose a hardware-software co-design on a 40 nm 256 Kb in-memory computing macro that physically integrates a fixed and random liquid state machine (LSM) SNN encoder with trainable artificial neural network (ANN) projections. We showcase the zero-shot LSM-based learning of multimodal events on the N-MNIST and N-TIDIGITS datasets, including visual and audio data association, as well as neural and visual data alignment for brain-machine interfaces. Our co-design achieves classification accuracy comparable to fully optimized software models, resulting in a 152.83 and 393.07-fold reduction in training costs compared to SOTA contrastive language-image pre-training (CLIP) and Prototypical networks, and a 23.34 and 160-fold improvement in energy efficiency compared to cutting-edge digital hardware, respectively. These proof-of-principle prototypes demonstrate zero-shot multimodal events learning capability for emerging efficient and compact neuromorphic hardware.
Ali Nikhalat-Jahromi, Ali Mohammad Saghiri, Mohammad Reza Meybodi
Learning Automaton (LA) is an adaptive self-organized model that improves its action-selection through interaction with an unknown environment. LA with finite action set can be classified into two main categories: fixed and variable structure. Furthermore, variable action-set learning automaton (VASLA) is one of the main subsets of variable structure learning automaton. In this paper, we propose VDHLA, a novel hybrid learning automaton model, which is a combination of fixed structure and variable action set learning automaton. In the proposed model, variable action set learning automaton can increase, decrease, or leave unchanged the depth of fixed structure learning automaton during the action switching phase. In addition, the depth of the proposed model can change in a symmetric (SVDHLA) or asymmetric (AVDHLA) manner. To the best of our knowledge, it is the first hybrid model that intelligently changes the depth of fixed structure learning automaton. Several computer simulations are conducted to study the performance of the proposed model with respect to the total number of rewards and action switching in stationary and non-stationary environments. The proposed model is compared with FSLA and VSLA. In order to determine the performance of the proposed model in a practical application, the selfish mining attack which threatens the incentive-compatibility of a proof-of-work based blockchain environment is considered. The proposed model is applied to defend against the selfish mining attack in Bitcoin and compared with the tie-breaking mechanism, which is a well-known defense. Simulation results in all environments have shown the superiority of the proposed model.
Verifying the correctness of computation without revealing the input is a critical issue intensively studied in real-world applications. The recent surge of zero knowledge arguments has been focusing on its efficiency and practicality. Among them, GKR-based arguments have received wide attention and become the foundation of many zero-knowledge proof protocols. However, GKR-based protocols are restricted to layered arithmetic circuits. We proposeTerrace, a new, efficient zero-knowledge argument system for general circuits, based on GKR. By dynamically patching cross-layer claims to the original circuit for verification instead of verifying those claims separately,Terraceis able to reduce the total circuit size and thus enjoys a logarithmic factor less verification time and proof size.Terraceis further extended to include the verification of non-arithmetic operations by rewriting those claims in the multilinear extension form. Experimental results demonstrate that Terrace enjoys a competitive performance on efficiency, and shows great promise in enabling low-cost verification of neural networks.
Neuromorphic intelligent systems are motivated by the observation that biological organisms - from algae to primates - excel in swiftly sensing their environment, reacting promptly to its perils and opportunities. Furthermore, biological organisms function more resiliently than our most advanced machines, with a fraction of their power requirements. Taking inspiration from how primates and humans have successfully evolved higher cognitive intelligence within social constructs, this paper proposes neuromorphic systems to be built and governed on a public distributed ledger platform. However, following in the footsteps of generic AI research, neuromorphic benchmarks and algorithms are developed in isolation. Furthermore, as a relatively niche research field, there is limited access to the actual neuromorphic sensors and large publicly available curated data, exacerbating the slow research progress. Nonetheless, centralized neuromorphic datasets and algorithms pose a threat to secure closed-loop behavior and learning outcomes, both commonly modulated in biological organisms via social interactions. This paper makes the case for early adoption of distributed ledger technology by neuromorphic systems and benchmarks to avoid the pitfalls endured by AI research - showcasing competing event-based gesture recognition systems on the Ethereum smart contract platform. This shift towards real-world and dynamic systems on a distributed ledger platform will improve collaboration among neuromorphic researchers while enabling healthy competition via incentives. Smart contract protocols allow model behavior monitoring, setting new learning tasks and increase in baseline performance, and naturally provides a governance framework for evolving neuromorphic systems. The code is publicly made available at: https://ist.github.com/BruceFan123.
To protect participants’ confidentiality, blockchains can be outfitted with anonymization methods. Observations of the underlying network traffic can identify the author of a transaction request, although these mechanisms often only consider the abstraction layer of blockchains. Previous systems either give topological confidentiality that may be compromised by an attacker in control of a large number of nodes, or provide strong cryptographic confidentiality but are so inefficient as to be practically unusable. In addition, there is no flexible mechanism to swap confidentiality for efficiency in order to accommodate practical demands. We propose a novel approach, the neural fairness protocol, which is a blockchain-based distributed ledger secured using neural networks and machine learning algorithms, enabling permissionless participation in the process of transition validation while concurrently providing strong assurance about the correct functioning of the entire network. Using cryptography and a custom implementation of elliptic curves, the protocol is designed to ensure the confidentiality of each transaction phase and peer-to-peer data exchange.
Chao Ni, Cong Tian, Kaiwen Yang, David Lo · 6 authors
Smart contract, a special software code running on and resided in the blockchain, enlarges the general application of blockchain and exchanges assets without dependence of external parties. With blockchain’s characteristic of immutability, they cannot be modified once deployed. Thus, the contract and the records are persisted on the blockchain forever, including failed transactions that are caused by runtime errors and result in the waste of computation, storage, and fees. In this paper, we refer to smart contracts which will cause runtime errors as crash-inducing smart contracts. However, automatic identification of crash-inducing smart contracts is limited investigated in the literature. The existing approaches to identify crash-inducing smart contracts are either limited in finding vulnerability (e.g., pattern-based static analysis) or very expensive (e.g., program analysis), which is insufficient for Ethereum.To reduce runtime errors on Ethereum, we propose an efficient, generalizable, and machine learning-based crash-inducing smart contract detector, CRASHSCDET, to automatically identify crash-inducing smart contracts. To investigate the effectiveness of CRASHSCDET, we firstly propose 34 static source code metrics from four dimensions (i.e., complexity metrics, count metrics, object-oriented metrics, and Solidity-specific metrics) to characterize smart contracts. Then, we collect a large-scale dataset of verified smart contracts (i.e., 54,739) and label these smart contracts based on their execution traces on Etherscan. We make a comprehensive comparison with three state-of-the-art approaches and the results show that CRASHSCDET can achieve good performance (i.e., 0.937 of F1-measure and 0.980 of AUC on average) and statistically significantly improve the baselines by 0.5%-60.4% in terms of F1-measure and by 41.2%-44.3% in terms of AUC, which indicates the effectiveness of static source code metrics in identifying crash-inducing smart contracts. We further investigate the importance of different types of metrics and find that metrics in different dimensions have varying abilities to depict the characteristic of smart contracts. Especially, metrics belonging to the "Count" dimension are the most discriminative ones but combining all metrics can achieve better prediction performance.
As the first blockchain platform supporting smart contracts, Ethereum has become increasingly popular in recent years and generates a massive number of transaction records. Nowadays, millions of accounts in Ethereum have been reported to participate in a variety of businesses, and some of them have been found to be involved in illegal behaviors or even cyber-crimes by exploiting the pseudonymous nature of blockchain. Therefore, there is an urgent need for an effective method to conduct account classification and audit transaction behaviors of each account. In this paper, we model the Ethereum transaction records as a transaction network, and the account classification problem is converted to a node classification problem. Based on the Ethereum transaction network, we propose a novel framework namedFilter andAugmentGraphNeuralNetwork (FA-GNN), which can retain the information of important neighbors and augment node features with high-order information. Experimental results demonstrate that our proposed FA-GNN outperforms state-of-the-art methods in Ethereum account classification.
Vikram Suresh, Chandra S. Katta, Srinivasan Rajagopalan, Tao Z. Zhou · 9 authors
Bitcoin is the leading blockchain-based cryptocurrency used to facilitate peer-to-peer transactions without relying on a centralized clearing house [1]. The conjoined process of transaction validation and currency minting, known as mining, employs the compute-intensive SHA256 double hash as proof-of-work. The one-way property of SHA256 necessitates a brute-force search by sweeping a 32b random input value called nonce. The 232nonce space search results in energy-intensive pool operations distributed on high-throughput mining systems, executing parallel nonce searches with candidate Merkle roots. Energy-efficient custom ASICs are required for cost-effective mining, where energy costs dominate operational expenses, and the number of hash engines integrated on a single die govern platform cost and peak mining throughput [2]. In this paper, we present BonanzaMine, an energy-efficient mining ASIC fabricated in 7nm CMOS (Fig. 21.3.7), featuring: (i) bitcoin-optimized look-ahead message digest datapath resulting in 33% Cdynreduction compared to conventional SHA256 digest datapath; (ii) a half-frequency scheduler datapath, reducing sequential and clock power by 33%; (iii) 3-phase latch-based design with stretchable non-overlapping clocks, eliminating min-delay paths; (iv) robust ultra-low-voltage operation at 355mV using board-level voltage-stacking; and (v) mining throughput of 137GHash/s at an energy efficiency of 55J/THash.
Zigui Jiang, Zibin Zheng, Kai Chen, Xiapu Luo · 6 authors
Since the development of Blockchain 2.0, the smart contract has become the core of blockchain. However, smart contracts with inaccurate or non-standard codes and settings may cause security vulnerabilities, extra expense cost and wast of computing resource. To avoid these problems and assist users to create new smart contract or apply existing smart contract in a more efficient way, we propose smart contract recommendation by regarding smart contract as a special form of software service in a blockchain system. First, four real-world datasets are obtained from Ethereum and EOSIO for smart contract recommendation. Then, a novel smart contract recommendation framework is proposed and evaluated. In the large-scale experiments, the results validate the feasibility of smart contract recommendation. Additionally, the datasets are publicly released online to other researchers for further studies on smart contract recommendation.
Deep neural networks (DNNs) could be very useful in blockchain applications such as DeFi and NFT trading. However, training / running large-scale DNNs as part of a smart contract is infeasible on today's blockchain platforms, due to two fundamental design issues of these platforms. First, blockchains nowadays typically require that each node maintain the complete world state at any time, meaning that the node must execute all transactions in every block. This is prohibitively expensive for computationally intensive smart contracts involving DNNs. Second, existing blockchain platforms expect smart contract transactions to have deterministic, reproducible results and effects. In contrast, DNNs are usually trained / run lock-free on massively parallel computing devices such as GPUs, TPUs and / or computing clusters, which often do not yield deterministic results. This paper proposes novel platform designs, collectively called A New Hope (ANH), that address the above issues. The main ideas are (i) computing-intensive smart contract transactions are only executed by nodes who need their results, or by specialized serviced providers, and (ii) a non-deterministic smart contract transaction leads to uncertain results, which can still be validated, though at a relatively high cost; specifically for DNNs, the validation cost can often be reduced by verifying properties of the results instead of their exact values. In addition, we discuss various implications of ANH, including its effects on token fungibility, sharding, private transactions, and the fundamental meaning of a smart contract.
Recently, the trending 5G technology encourages extensive applications of on-device machine learning , which collects user data for model training. This requires cost-effective techniques to preserve the privacy and the security of model training within the resource-constrained environment. Traditional learning methods rely on the trust among the system for privacy and security. However, with the increase of the learning scale, maintaining every edge device’s trustworthiness could be expensive. To cost-effectively establish trust in a trustless environment, this paper proposes democratic learning (DemL), which makes the first step to explore hardware/software co-design for blockchain-secured decentralized on-device learning. By utilizing blockchain’s decentralization and tamper-proofing, our design secures AI learning in a trustless environment. To tackle the extra overhead introduced by blockchain , we propose PoMC (an algorithm and architecture co-design) as a novel blockchain consensus mechanism , which first exploits cross-domain reuse (AI learning and blockchain consensus) in AI learning architecture. Evaluation results show our DemL can protect AI learning from privacy leakage and model pollution, and demonstrated that privacy and security come with trivial hardware overhead and power consumption (2%). We believe that our work will open the door of synergizing blockchain and on-device learning for security and privacy.
This paper proposes a new approach and tool for blockchain software developers and architects to use for determining which computations of a smart contract can be effectively done off-chain without loss of trust and how they can be moved off-chain automatically. Our approach uses Finite State Machines (FSMs) and Hierarchical State Machines (HSMs) modeling in order to create smart contract patterns using graphs and then use pattern properties to identify which parts of the smart contracts should be considered for moving off-chain. Once software execution transits into the pattern's entry state, off-chain execution continues until the execution transits into the pattern's exit state, when execution continues on-chain. This paper further describes a tool for (1) FSM input and visualization, its analysis, and automatic transformation into a smart contract, and (2) an interface to support the developers' decision to optimally off-chain computations and to (3) define the communications between on-chain and off-chain computations.