Dishonest Approximate Computing: A Coming Crisis for Cloud Clients

TL;DR

The paper tackles the risk that Approximate Computing (AC) could be misused by untrusted cloud service providers to deliver cheaper AC services while falsely presenting them as accurate computations. It introduces two golden-model-free detectors, Residual Class Check (RCC) and Forward-Backward Check (FBC), designed to run alongside real tasks to detect DisHonest Approximate Computing (DHAC) in real time without a golden reference. RCC leverages arithmetic in residual class rings $Z_m$ to verify results via multi-round checks, while FBC instruments reversible sentinel branches to expose deviations in floating-point computations. Empirical results show DHAC detection accuracies of 96–99% across integer and floating-point scenarios, with RCC achieving high reliability and FBC proving effective for FP programs, suggesting practical applicability for cloud clients to guard against DHAC with low overhead and no trusted model.

Abstract

Approximate Computing (AC) has emerged as a promising technique for achieving energy-efficient architectures and is expected to become an effective technique for reducing the electricity cost for cloud service providers (CSP). However, the potential misuse of AC has not received adequate attention, which is a coming crisis behind the blueprint of AC. Driven by the pursuit of illegal financial profits, untrusted CSPs may deploy low-cost AC devices and deceive clients by presenting AC services as promised accurate computing products, while falsely claiming AC outputs as accurate results. This misuse of AC will cause both financial loss and computing degradation to cloud clients. In this paper, we define this malicious attack as DisHonest Approximate Computing (DHAC) and analyze the technical challenges faced by clients in detecting such attacks. To address this issue, we propose two golden model free detection methods: Residual Class Check (RCC) and Forward-Backward Check (FBC). RCC provides clients a low-cost approach to infer the residual class to which a legitimate accurate output should belong. By comparing the residual class of the returned result, clients can determine whether a computing service contains any AC elements. FBC detects potential DHAC by computing an invertible check branch using the intermediate values of the program. It compares the values before entering and after returning from the check branch to identify any discrepancies. Both RCC and FBC can be executed concurrently with real computing tasks, enabling real-time DHAC detection with current inputs. Our experimental results show that both RCC and FBC can detect over 96%-99% of DHAC cases without misjudging any legitimate accurate results.

Figures (9)

• Figure 1: Dishonest Approximate Computing
• Figure 2: Approximation Feature Masking
• Figure 3: The Workflow of RCC Process
• Figure 4: Check Segment Extracting
• Figure 5: The Workflow of FBC Process