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This is the first work that incorporates recent advancements in "explainability" of machine learning (ML) to build a routing obfuscator called ObfusX. We adopt a recent metric---the SHAP value---which explains to what extent each layout feature can reveal each unknown connection for a recent ML-based split manufacturing attack model. The unique benefits of SHAP-based analysis include the ability to identify the best candidates for obfuscation, together with the dominant layout features which make them vulnerable. As a result, ObfusX can achieve better hit rate (97% lower) while perturbing significantly fewer nets when obfuscating using a via perturbation scheme, compared to prior work. When imposing the same wirelength limit using a wire lifting scheme, ObfusX performs significantly better in performance metrics (e.g., 2.4 times more reduction on average in percentage of netlist recovery).

We propose a framework for Class-aware Personalized Neural Network Inference (CAP’NN), which prunes an already-trained neural network model based on the preferences of individual users. Specifically, by adapting to the subset of output classes that each user is expected to encounter, CAP’NN is able to prune not only ineffectual neurons but also miseffectual neurons that confuse classification, without the need to retrain the network. CAP’NN also exploits the similarities among pruning requests from different users to minimize the timing overheads of pruning the network. To achieve this, we propose a clustering algorithm that groups similar classes in the network based on the firing rates of neurons for each class and then implement a lightweight cache architecture to store and reuse information from previously pruned networks. In our experiments with VGG-16, AlexNet, and ResNet-152 networks, CAP’NN achieves, on average, up to 47% model size reduction while actually improving the top-1(5) classification accuracy by up to 3.9%(3.4%) when the user only encounters a subset of the trained classes in these networks.

In this work, we propose AirNN, a novel framework which enables dynamic approximation of an already-trained convolutional neural network (CNN) in hardware during inference. AirNN enables input-dependent approximation of the CNN to achieve energy saving without much degradation in its classification accuracy at runtime. For each input, AirNN uses only a fraction of the CNN’s weights based on that input (with the rest remaining 0) to conduct the inference. Consequently, energy saving is possible due to fewer number of fetches from off-chip memory as well as fewer multiplications for majority of the inputs. To achieve per-input approximation, we propose a clustering algorithm that groups similar weights in the CNN based on their importance, and design an iterative framework that decides dynamically how many clusters of weights should be fetched from off-chip memory for each individual input. We also propose new hardware structures to implement our framework on top of a recently proposed FPGA-based CNN accelerator. In our experiments with popular CNNs, we, on average, show 49% energy saving with less than 3% degradation in classification accuracy due to doing inference with only a fraction of the weights for the majority of the inputs. We also propose a greedy interleaving scheme, implemented in hardware, in order to improve the performance of the iterative procedure and compensate for its latency overhead.