We consider an integrated sensing-communication system operating in a dynamic environment, such as an autonomous vehicle scenario. We propose a novel, low-cost, low power consumption and low-computation approach for designing a beam that can simultaneously reach the radar target of interest and the desired communication destination. The transmitter is a uniform linear array, equipped with quantized double phase shifters, which enables a flexible beam design while using analog only processing. Only a small number of antennas are selected to transmit in each channel use, in order to save system power and reduce antenna coupling. We propose a deep reinforcement learning approach to adaptively adjust the double phase shifters and select the active antennas in order to optimize the transmit beamforming, through a transmission and feedback trail. The actor-critic network strategy together with the Wolpertinger policy is adopted to obtain the optimal solutions efficiently and effectively. Numerical results demonstrate the feasibility of the proposed method.

Malicious applications have become increasingly numerous. This demands adaptive, learning-based techniques for constructing malware detection engines, instead of the traditional manual-based strategies. Prior work in learning-based malware detection engines primarily focuses on dynamic trace analysis and byte-level n-grams. Our approach in this paper differs in that we use compiler intermediate representations, i.e., the callgraph representation of binaries. Using graph-based program representations for learning provides structure of the program, which can be used to learn more advanced patterns. We use the Shortest Path Graph Kernel (SPGK) to identify similarities between call graphs extracted from binaries. The output similarity matrix is fed into a Support Vector Machine (SVM) algorithm to construct highly-accurate models to predict whether a binary is malicious or not. However, SPGK is computationally expensive due to the size of the input graphs. Therefore, we evaluate different parallelization methods for CPUs and GPUs to speed up this kernel, allowing us to continuously construct up-to-date models in a timely manner. Our hybrid implementation, which leverages both CPU and GPU, yields the best performance, achieving up to a 14.2x improvement over our already optimized OpenMP version. We compared our generated graph-based models to previously state-of-the-art feature vector 2-gram and 3-gram models on a dataset consisting of over 22,000 binaries. We show that our classification accuracy using graphs is over 19% higher than either n-gram model and gives a false positive rate (FPR) of less than 0.1%. We are also able to consider large call graphs and dataset sizes because of the reduced execution time of our parallelized SPGK implementation.

Malware classification for the Android ecosystem can be performed using a range of techniques. One major technique that has been gaining ground recently is dynamic analysis based on system call invocations recorded during the executions of Android applications. Dynamic analysis has traditionally been based on converting system calls into flat feature vectors and feeding the vectors into machine learning algorithms for classification. In this paper, we implement three traditional feature-vector-based representations for Android system calls. For each feature vector representation, we also propose a novel graph-based representation. We then use graph kernels to compute pair-wise similarities and feed these similarity measures into a Support Vector Machine (SVM) for classification. To speed up the graph kernel computation, we compress the graphs using the Compressed Row Storage format, and then we apply OpenMP to parallelize the computation. Experiments show that the graph-based representations are able to improve the classification accuracy over the corresponding feature-vector-based representations from the same input. Finally we show that different representations can be combined together to further improve classification accuracy.

As computation becomes increasingly limited by data movement and energy consumption, exploiting locality throughout the memory hierarchy becomes critical to continued performance scaling. Moving computation closer to memory presents an opportunity to reduce both energy and data movement overheads. We explore the use of 3D die stacking to move memory-intensive computations closer to memory. This approach to processing in memory addresses some drawbacks of prior research on in-memory computing and is commercially viable in the foreseeable future.

The Coarse-Grained Monte Carlo (CGMC) method is a multi-scale stochastic mathematical and simulation framework for spatially distributed systems. CGMC simulations are important tools for studying phenomena such as catalysis, crystal growth, surface diffusion, phase transitions on single crystals, and cell membrane receptor dynamics. In parallel CGMC, the tau-leap method is used for parallel simulations that are executed on traditional CPU clusters in a master-slave setting. Unfortunately the communications between master and slaves negatively impact speedup and scalability. In this paper, we explore the potentials of GPUs for the tau-leap method and we present an extensive performance evaluation that leads to the most suitable degree of parallelism for this method under different simulation profiles. We show how the efficient parallelization of the tau-leap method for GPUs includes (1) the redefinition of its data structures, (2) the redesign of its algorithm, and (3) the selection of the most appropriate degree of parallelism (i.e., fine-grained or course-gained) on a single GPU or multiple GPUs. Exceptional performance improvements can thus be achieved for this method.