Error Adaptive Classifier Boosting (EACB): Leveraging Data-Driven Training Towards Hardware Resilience for Signal Inference
Error Adaptive Classifier Boosting (EACB): Leveraging Data-Driven Training Towards Hardware Resilience for Signal Inference
复制标题
误差自适应分类器增强 (EACB)：利用数据驱动训练实现信号推理的硬件弹性
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
N. Verma
中科院分区：
文献类型：
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作者：
Zhuo Wang;R. Schapire;N. Verma
CMOS技术的持续扩展和后CMOS技术的考虑将硬件可靠性提升到一流的挑战，特别是在能源和资源受限的嵌入式传感器应用中。在这样的应用程序中，越来越强调推理函数。机器学习算法通过构建数据驱动模型来对过于复杂而无法进行分析建模的数据进行推理，从而发挥重要作用。本文探讨了如何利用数据驱动训练来克服在推理阶段由于硬件故障引起的计算错误。随机故障注入的FPGA仿真表明，所提出的架构将系统性能恢复到无故障系统的水平，其中1%的硬件需要显式故障保护，而数字故障影响其余硬件中2%的电路节点。为了训练一个错误感知推理模型，提出了一种训练算法，与之前报道的算法（分别为AdaBoost和FilterBoost）相比，该算法的硬件（内存）和能量要求降低了65倍和10倍，从而可以完全在设备上构建模型。
The continued scaling of CMOS technologies and consideration of post-CMOS technologies has elevated hardware reliability to a first-class challenge, particularly in energy- and resource-constrained embedded sensor applications. In such applications, there is an increasing emphasis on inference functions. Machine-learning algorithms play an important role by enabling the construction of data-driven models for inference over data that is too complex to model analytically. This paper explores how data-driven training can be exploited to also overcome computational errors due to hardware faults within an inference stage. FPGA emulation with randomized fault injections shows that the proposed architecture restores system performance to the level of a fault free system, with 1% of the hardware requiring explicit fault protection, and with digital faults affecting >2% of the circuit nodes in the rest of the hardware. To train an error-aware inference model, a training algorithm is presented whose hardware (memory) and energy requirements are reduced by 65 × and 10 × compared to previously reported algorithms (AdaBoost and FilterBoost respectively), thereby enabling model construction entirely on the device.