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The reliability of emerging neuromorphic compute fabrics is of great concern due to their widespread use in critical data-intensive applications. Ensuring such reliability is difficult due to the intensity of underlying computations (billions of parameters), errors induced by low power operation and the complex relationship between errors in computations and their effect on network performance accuracy. We study the problem of designing error-resilient neuromorphic systems where errors can stem from: (a) soft errors in computation of matrix-vector multiplications and neuron activations, (b) malicious trojan and adversarial security attacks and (c) effects of manufacturing process variations on analog crossbar arrays that can affect DNN accuracy. The core principle of error detection relies on embedded predictive neuron checks using invariants derived from the statistics of nominal neuron activation patterns of hidden layers of a neural network. Algorithmic encodings of hidden neuron function are also used to derive invariants for checking. A key contribution is designing checks that are robust to the inherent nonlinearity of neuron computations with minimal impact on error detection coverage. Once errors are detected, they are corrected using probabilistic methods due to the difficulties involved in exact error diagnosis in such complex systems. The technique is scalable across soft errors as well as a range of security attacks. The effects of manufacturing process variations are handled through the use of compact tests from which DNN performance can be assessed using learning techniques. Experimental results on a variety of neuromorphic test systems: DNNs, spiking networks and hyperdimensional computing are presented.
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Encoded Check Driven Concurrent Error Detection in Particle Filters for Nonlinear State Estimation
In this paper we propose a framework for concurrent detection of soft computation errors in particle filters which are finding increasing use in robotics applications. The particle filter works by sampling the multi-variate probability distribution of the states of a system (samples called particles, each particle representing a vector of states) and projecting these into the future using appropriate nonlinear mappings. We propose the addition of a `check' state to the system as a linear combination of the system states for error detection. The check state produces an error signal corresponding to each particle, whose statistics are tracked across a sliding time window. Shifts in the error statistics across all particles are used to detect soft computation errors as well as anomalous sensor measurements. Simulation studies indicate that errors in particle filter computations can be detected with high coverage and low latency.
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- Award ID(s):
- 1723997
- PAR ID:
- 10274775
- Date Published:
- Journal Name:
- International On-Line Testing Conference
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IOLTS50870.2020.9159724
