Presentation
E³-CODE: Embedded and Efficient Error-Correcting Code for Error-Resilient Neural Networks
DescriptionNeural networks are widely deployed at the edge to process high-
dimensional sensor data, but they are susceptible to burst errors that
can corrupt weights and degrade inference accuracy. Conventional
error-correcting codes (ECC) mitigate errors but incur significant
memory overhead. Recent ECC methods for neural networks over-
write the least significant bits of the model weights with parity bits,
providing zero-overhead resilience at the expense of slightly re-
duced inference accuracy. In this paper, we propose a framework for
Embedded and Efficient Error-Correcting Code for Error-Resilient
Neural Networks called (E3-CODE). The proposed method embeds
multi-bit parity within the entire weight representation, which is
different from only modifying the LSBs of the weights. To mini-
mize the negative impact from the parity embedded ECC, weight
and parity assignments are jointly optimized via a mixed-integer
linear programming (MILP) formulation. We also propose a hybrid
ECC scheme that combines the embedded ECC with conventional
ECC to trade-off minor memory overhead for significantly im-
proved reliance. The experimental evaluation on the ImageNet and
CIFAR-10 datasets using ResNet, MobileNetV2, and EfficientNet-B0
demonstrates that E3-CODE maintains software-level accuracy in
the presence of burst errors. Compared with prior methods, the
lifetime of the edge system is extended by 4.8𝑋 with no memory
overhead and 10𝑋 with less than 2% memory overhead.
dimensional sensor data, but they are susceptible to burst errors that
can corrupt weights and degrade inference accuracy. Conventional
error-correcting codes (ECC) mitigate errors but incur significant
memory overhead. Recent ECC methods for neural networks over-
write the least significant bits of the model weights with parity bits,
providing zero-overhead resilience at the expense of slightly re-
duced inference accuracy. In this paper, we propose a framework for
Embedded and Efficient Error-Correcting Code for Error-Resilient
Neural Networks called (E3-CODE). The proposed method embeds
multi-bit parity within the entire weight representation, which is
different from only modifying the LSBs of the weights. To mini-
mize the negative impact from the parity embedded ECC, weight
and parity assignments are jointly optimized via a mixed-integer
linear programming (MILP) formulation. We also propose a hybrid
ECC scheme that combines the embedded ECC with conventional
ECC to trade-off minor memory overhead for significantly im-
proved reliance. The experimental evaluation on the ImageNet and
CIFAR-10 datasets using ResNet, MobileNetV2, and EfficientNet-B0
demonstrates that E3-CODE maintains software-level accuracy in
the presence of burst errors. Compared with prior methods, the
lifetime of the edge system is extended by 4.8𝑋 with no memory
overhead and 10𝑋 with less than 2% memory overhead.
Event Type
Research Manuscript
TimeTuesday, July 2810:30am - 10:43am PDT
LocationMtg Room 202C