Quantum circuit simulator (QCS) is essential for designing quantum algorithms because it assists researchers in understanding how quantum operations work without access to expensive quantum computers. Traditional array-based QCSs suffer from exponential time and memory complexities. To address this problem, Decision Diagram (DD) was introduced to compress simulation data by exploring the circuit regularity. However, for irregular circuit structures, DD-based simulation incurs significant runtime and memory overhead. To overcome this challenge, we present FlatDD, a high-performance QCS that capitalizes on the strength of both DD- and array-based approaches. FlatDD parallelizes the simulation workload at multiple levels and leverages an efficient caching technique to reuse historical results. To further enhance the simulation performance for deep circuits, FlatDD introduces a gate-fusion algorithm to reduce the computational cost. Compared to state-of-the-art QCSs on commonly used quantum circuits, FlatDD achieves 34.81× speed-up and 1.93× memory reduction.

Recent studies have exposed that voice assistants can be manipulated by various voice commands without being noticed, however, existing attacks require a nearby speaker to play the attack commands. In this paper, we demonstrate that even without a speaker, we can use capacitors inside electronic devices to produce malicious voice commands, i.e., we convert capacitors into speakers and call it CapSpeaker . The underlying principle of CapSpeaker is the inverse piezoelectric effect, i.e., varying the voltage across a capacitor to make it vibrate and thus emit acoustic noises. Forcing capacitors to emit target voice commands is challenging because (1) capacitors' response frequency is out of the range of audible voices. (2) We can not directly control the voltage across capacitors to manipulate their emit sounds. To overcome these challenges, we propose a PWM-based modulation scheme to embed the malicious audio onto a high-frequency carrier, e.g., above 20 kHz, and we create malware to induce the designed voltage across the capacitors such that CapSpeaker plays the chosen malicious commands. Our evaluation of 7 commercial devices demonstrates that CapSpeaker is feasible to inject voice commands, e.g., ”open the door”, at a distance of up to 10.5 cm.

Graph partitioning is important for the design of many CAD algorithms. However, as the graph size continues to grow, graph partitioning becomes increasingly time-consuming. To overcome these challenges, we propose G-kway, an efficient multilevel GPUaccelerated.. -way graph partitioner. G-kway introduces an effective union find-based coarsening and a novel independent set-based refinement algorithm to significantly accelerate both the coarsening and uncoarsening stages. Experimental results have shown that G-kway outperforms both the state-of-the-art CPU-based and GPUbased parallel partitioners with an average speedup of 8.6x and 3.8x, respectively, while achieving comparable partitioning quality.

Scan chains have greatly improved hardware testability while introducing security breaches for confidential data. Scan-chain attacks have extended their scope from cryptoprocessors to AI edge devices. The recently proposed scan-chain-based neural network (NN) model extraction attack (lCCAD 2021) made it possible to achieve fine-grained extraction and is multiple orders of magnitude more efficient both in queries and accuracy than its coarse-grained mathematical counterparts. However, both query formulation complexity and constraint solver failures increase drastically with network depth/size. We demonstrate a more powerful adversary, who is capable of improving scalability while maintaining accuracy, by relaxing high-fidelity constraints to formulate an approximate-fidelity-based layer-constrained least-squares extraction using random queries. We conduct our extraction attack on neural network inference topologies of different depths and sizes, targeting the MNIST digit recognition task. The results show that our method outperforms the scan-chain attack proposed in ICCAD 2021 by an average increase in the extracted neural network's functional accuracy of ≈ 32% and 2–3 orders of reduction in queries. Furthermore, we demonstrated that our attack is highly effective even in the presence of countermeasures against adversarial samples.

Sparse deep neural networks (DNNs) leverage sparse representations to achieve faster inference and lower memory footprint. However, deploying sparse DNNs comes with challenges, such as irregular memory access patterns, workload imbalance, etc. To address these challenges, IEEE HPEC has organized the Sparse DNN Graph Challenge (SDGC), seeking new methods from the high-performance computing community. For many years, SDGC has yielded innovative works on accelerating sparse DNN inference. However, none of them have identified redundant global memory access that contributes to significant runtime overhead. To overcome this challenge, we propose GLARE, a framework that can assist existing sparse inference kernels in effectively reducing redundant global memory access. We have applied GLARE to previous SDGC champions and a recent sparse inference engine SNICIT. Evaluated on SDGC benchmarks, we demonstrate the promising performance of GLARE and its generalizability in accelerating existing sparse inference kernels, for instance, up to 31.56 x speed-up over one of the previous SDGC champions.

Due to cost benefits, supply chains of integrated circuits (ICs) are largely outsourced nowadays. However, passing ICs through various third-party providers gives rise to many threats, like piracy of IC intellectual property or insertion of hardware Trojans, i.e., malicious circuit modifications. In this work, we proactively and systematically harden the physical layouts of ICs against post-design insertion of Trojans. Toward that end, we propose a multiplexer-based logic-locking scheme that is (i) devised for layout-level Trojan prevention, (ii) resilient against state-of-the-art, oracle-less machine learning attacks, and (iii) fully integrated into a tailored, yet generic, commercial-grade design flow. Our work provides in-depth security and layout analysis on a challenging benchmark suite. We show that ours can render layouts resilient, with reasonable overheads, against Trojan insertion in general and also against second-order attacks (i.e., adversaries seeking to bypass the locking defense in an oracle-less setting). We release our layout artifacts for independent verification [29].

Sparse deep neural network (DNN) has become an important technique for reducing the inference cost of large DNNs. However, computing large sparse DNNs is very challenging because inference iterations can incur highly irregular patterns and unbalanced loads. To address this challenge, the recent HPEC Graph Challenge seeks novel high-performance inference methods for large sparse DNNs. Despite the rapid progress over the past four years, solutions have largely focused on static model compression or sparse multiplication kernels, while ignoring dynamic data compression at inference time which can achieve significant yet untapped performance benefits. Consequently, we propose SNICIT, a new GPU algorithm to accelerate large sparse DNN inference via compression at inference time. SNICIT leverages data clustering to transform intermediate results into a sparser representation that largely reduces computation over inference iterations. Evaluated on both HPEC Graph Challenge benchmarks and conventional DNNs (MNIST, CIFAR-10), SNICIT achieves 6 ∼ 444 × and 1.36 ∼ 1.95 × speed-ups over the previous champions, respectively.

Ransomware is a kind of computer malware that has spread widely in recent years, such as DarkSide, which spread around the world recently. It’s reported that DarkSide extorted ${\$}$ 90 million in nine months. It extorts ransom from users by encrypting user files and other methods, causing huge economic losses to users, including commercial organizations and individuals. Existing ransomware detection methods include the hostbased methods and the network-based methods. However, these methods are either hard to deploy or have the possibility to be evaded. In this paper, we propose OutletGuarder, a non-intrusive detection method against DarkSide ransomware based on the signal generated by the Power Factor Correction module of the host computer’s power supply in electrical outlets, which carries the power consumption information of the host computer during the execution of DarkSide. By utilizing the power consumption variation among different programs, especially the power consumption caused by frequent encryption and I/O operations during the execution of DarkSide, OutletGuarder achieves a detection F1 Score of 97.50%. The impact of classification models and untrained programs, as well as the model transferability and robustness are evaluated.

Voice assistants can be manipulated by various malicious voice commands, yet existing attacks require a nearby speaker to play the attack commands. In this paper, we show that even when no speakers are available, we can play malicious commands by utilizing the capacitors inside electronic devices, i.e., we convert capacitors into speakers and call it CapSpeaker. Essentially, capacitors can emit acoustic noises due to the inverse piezoelectric effect, i.e., varying the voltage across a capacitor can make it vibrate and thus emit acoustic noises. Forcing capacitors to play malicious voice commands is challenging because (1) the frequency responses of capacitors as speakers have poor performance in the range of audible voices, and (2) we have no direct control over the voltage across capacitors to manipulate their emitting sounds. To overcome the challenges, we use a PWM-based modulation scheme to embed the malicious audio onto a high-frequency carrier, e.g., above 20 kHz, and we create malware that can induce the right voltage across the capacitors such that CapSpeaker plays the chosen malicious commands. We conducted extensive experiments with 2 LED lamps (a modified one and a commercial one) and 5 victim devices (iPhone 4s, iPad mini 5, Huawei Nova 5i, etc.). Evaluation results demonstrate that CapSpeaker is feasible at a distance up to 10.5 cm, triggering a smartphone to receive voice commands, e.g., "open the door''.