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Spiking neural networks (SNNs) are quickly gaining traction as a viable alternative to deep neural networks (DNNs). Compared to DNNs, SNNs are computationally more powerful and energy efficient. The design metrics (synaptic weights, membrane threshold, etc.) chosen for such SNN architectures are often proprietary and constitute confidential intellectual property (IP). Our study indicates that SNN architectures implemented using conventional analog neurons are susceptible to side channel attack (SCA). Unlike the conventional SCAs that are aimed to leak private keys from cryptographic implementations, SCANN (SCA̲ of spiking n̲eural n̲etworks) can reveal the sensitive IP implemented within the SNN through the power side channel. We demonstrate eight unique SCANN attacks by taking a common analog neuron (axon hillock neuron) as the test case. We chose this particular model since it is biologically plausible and is hence a good fit for SNNs. Simulation results indicate that different synaptic weights, neurons/layer, neuron membrane thresholds, and neuron capacitor sizes (which are the building blocks of SNN) yield distinct power and spike timing signatures, making them vulnerable to SCA. We show that an adversary can use templates (using foundry-calibrated simulations or fabricating known design parameters in test chips) and analysis to identify the specifications of the implemented SNN. 

The size of transistors has drastically reduced over the years. Interconnects have likewise also been scaled down. Today, conventional copper (Cu)-based interconnects face a significant impediment to further scaling since their electrical conductivity decreases at smaller dimensions, which also worsens the signal delay and energy consumption. As a result, alternative scalable materials such as semi-metals and 2D materials were being investigated as potential Cu replacements. In this paper, we experimentally showed that CoPt can provide better resistivity than Cu at thin dimensions and proposed hybrid poly-Si with a CoPt coating for local routing in standard cells for compactness. We evaluated the performance gain for DRAM/eDRAM, and area vs. performance trade-off for D-Flip-Flop (DFF) using hybrid poly-Si with a thin film of CoPt. We gained up to a 3-fold reduction in delay and a 15.6% reduction in cell area with the proposed hybrid interconnect. We also studied the system-level interconnect design using NbAs, a topological semi-metal with high electron mobility at the nanoscale, and demonstrated its advantages over Cu in terms of resistivity, propagation delay, and slew rate. Our simulations revealed that NbAs could reduce the propagation delay by up to 35.88%. We further evaluated the potential system-level performance gain for NbAs-based interconnects in cache memories and observed an instructions per cycle (IPC) improvement of up to 23.8%. 

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).