Hash functions like SHA-1 or MD5 are one of the most important cryptographic primitives, especially in the field of information integrity. Considering the fact that increasing methods have been proposed to break these hash algorithms, a competition for a new family of hash functions was held by the US National Institute of Standards and Technology. Keccak was the winner and selected to be the next generation of hash function standard, named SHA-3. We aim to implement and optimize Batch mode based Keccak algorithms on NVIDIA GPU platform. Our work consider the case of processing multiple hash tasks at once and implement the case on CPU and GPU respectively. Our experimental results show that GPU performance is significantly higher than CPU is the case of processing large batches of small hash tasks.
, @cite_12 contribute to the cryptography research community by presenting techniques to accelerate symmetric block ciphers (IDEA, Blowfish and Threefish) in NVIDIA GTX 690 with Kepler architecture. The results are benchmarked against implementation in OpenMP and existing GPU implementations in the literature. We are able to achieve encryption throughput of 90.3 Gbps, 50.82 Gbps and 83.71 Gbps for IDEA, Blowfish and Threefish respectively. Block ciphers can be used as pseudorandom number generator (PRNG) when it is operating under counter mode (CTR), but the speed is usually slower compare to other PRNG using lighter operations. Hence, they attempt to modify IDEA and Blowfish in order to achieve faster PRNG generation. The modified IDEA and Blowfish manage to pass all NIST Statistical Test and TestU01 Small Crush except the more stringent tests in TestU01 (Crush and BigCrush).
@cite_12
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GPU is widely used in various applications that require huge computational power. In this paper, we contribute to the cryptography research community by presenting techniques to accelerate symmetric block ciphers (IDEA, Blowfish and Threefish) in NVIDIA GTX 690 with Kepler architecture. The results are benchmarked against implementation in OpenMP and existing GPU implementations in the literature. We are able to achieve encryption throughput of 90.3 Gbps, 50.82 Gbps and 83.71 Gbps for IDEA, Blowfish and Threefish respectively. Block ciphers can be used as pseudorandom number generator (PRNG) when it is operating under counter mode (CTR), but the speed is usually slower compare to other PRNG using lighter operations. Hence, we attempt to modify IDEA and Blowfish in order to achieve faster PRNG generation. The modified IDEA and Blowfish manage to pass all NIST Statistical Test and TestU01 Small Crush except the more stringent tests in TestU01 (Crush and BigCrush). The importance of cryptography on ensuring security or integrity of the electronic data transaction had become higher during the past few years. Multiple security protocols are currently using various block ciphers. One of the most widely used block ciphers is the Advanced Encryption Standard (AES) which is chosen as a standard for its higher efficiency and stronger security than its competitors. Unfortunately, the encryption and decryption processes of AES takes a considerable amount of time for large data size. The GPU is an attractive platform for accelerating block ciphers and other cryptography algorithms due to its massively parallel processing power. In this work, an implementation of the AES-128 ECB Encryption on three different GPU architectures (Kepler, Maxwell and Pascal) has been presented. The results show that encryption speeds with 207 Gbps on the NVIDIA GTX TITAN X (Maxwell) and 280 Gbps on the NVIDIA GTX 1080 (Pascal) have been achieved by performing new optimization techniques using 32bytes thread granularity. The Advanced Encryption Standard (AES) is One of the most popular symmetric block cipher because it has better efficiency and security. The AES is computation intensive algorithm especially for massive transactions. The Graphics Processing Unit (GPU) is an amazing platform for accelerating AES. it has good parallel processing power. Traditional approaches for implementing AES using GPU use 16 byte per thread as a default granularity. In this paper, the AES-128 algorithm (ECB mode) is implemented on three different GPU architectures with different values of granularities (32,64 and 128 bytes thread). Our results show that the throughput factor reaches 277 Gbps, 201 Gbps and 78 Gbps using the NVIDIA GTX 1080 (Pascal), the NVIDIA GTX TITAN X (Maxwell) and the GTX 780 (Kepler) GPU architectures. Research has shown that convolutional neural networks contain significant redundancy, and high classification accuracy can be obtained even when weights and activations are reduced from floating point to binary values. In this paper, we present FINN, a framework for building fast and flexible FPGA accelerators using a flexible heterogeneous streaming architecture. By utilizing a novel set of optimizations that enable efficient mapping of binarized neural networks to hardware, we implement fully connected, convolutional and pooling layers, with per-layer compute resources being tailored to user-provided throughput requirements. On a ZC706 embedded FPGA platform drawing less than 25 W total system power, we demonstrate up to 12.3 million image classifications per second with 0.31 μs latency on the MNIST dataset with 95.8 accuracy, and 21906 image classifications per second with 283 μs latency on the CIFAR-10 and SVHN datasets with respectively 80.1 and 94.9 accuracy. To the best of our knowledge, ours are the fastest classification rates reported to date on these benchmarks.