1,379 research outputs found
swTVM: Exploring the Automated Compilation for Deep Learning on Sunway Architecture
The flourish of deep learning frameworks and hardware platforms has been
demanding an efficient compiler that can shield the diversity in both software
and hardware in order to provide application portability. Among the exiting
deep learning compilers, TVM is well known for its efficiency in code
generation and optimization across diverse hardware devices. In the meanwhile,
the Sunway many-core processor renders itself as a competitive candidate for
its attractive computational power in both scientific and deep learning
applications. This paper combines the trends in these two directions.
Specifically, we propose swTVM that extends the original TVM to support
ahead-of-time compilation for architecture requiring cross-compilation such as
Sunway. In addition, we leverage the architecture features during the
compilation such as core group for massive parallelism, DMA for high bandwidth
memory transfer and local device memory for data locality, in order to generate
efficient code for deep learning application on Sunway. The experimental
results show the ability of swTVM to automatically generate code for various
deep neural network models on Sunway. The performance of automatically
generated code for AlexNet and VGG-19 by swTVM achieves 6.71x and 2.45x speedup
on average than hand-optimized OpenACC implementations on convolution and fully
connected layers respectively. This work is the first attempt from the compiler
perspective to bridge the gap of deep learning and high performance
architecture particularly with productivity and efficiency in mind. We would
like to open source the implementation so that more people can embrace the
power of deep learning compiler and Sunway many-core processor
Static Analysis Based Behavioral API for Malware Detection using Markov Chain
Researchers employ behavior based malware detection models that depend on API tracking and analyzing features to identify suspected PE applications. Those malware behavior models become more efficient than the signature based malware detection systems for detecting unknown malwares. This is because a simple polymorphic or metamorphic malware can defeat signature based detection systems easily. The growing number of computer malwares and the detection of malware have been the concern for security researchers for a large period of time. The use of logic formulae to model the malware behaviors is one of the most encouraging recent developments in malware research, which provides alternatives to classic virus detection methods. To address the limitation of traditional AVs, we proposed a virus detection system based on extracting Application Program Interface (API) calls from virus behaviors. The proposed research uses static analysis of behavior-based detection mechanism without executing of software to detect viruses at user mod by using Markov Chain. Keywords: Malware Detection; Markov Chain; Virus Behavior; API Call
- …