RISC-V架构的一种指令自动对齐电路

基于RISC-V指令集架构实现了一种指令自动对齐电路。该电路可以将指令缓存发送过来的32位指令数据进行分解,从中解析出正确的指令。当指令缓存发送过来的32位指令数据对应的地址不是4字节对齐时(地址的低两位不是00)时,该电路可以自动将下一拍取指令数据的地址对齐到4字节,同时给出指令是否有效的指示标识;当指令地址4字节对齐时,该电路对指令数据进行分析,给出指令有效的标识、指令和对应指令的实际地址。给出的指令对齐电路的延时为4级两输入逻辑门,适用于高频标量处理器的前端取指令电路。     

SDRAM视频存储控制器的设计与实现

提出了一种3DDCT视频压缩核的SDRAM接口存储器控制模块实现方案。该方案利用状态机完成SDRAM接口信号编码以及缓冲数据接口的FIFO操作。     

基于状态机的SDRAM控制器的设计与实现

现代计算机的基本框架仍是以冯·诺伊曼结构为基础,以中央控制单元和存储指令/数据的存储器之间的通信为支撑的。同步动态随机存储器(即SDRAM)与静态RAM相比具有容量大,成本低的优势;与传统异步DRAM相比其速度更快,所以得到了越来越广泛的应用。因此以简化主机对SDRAM访问为主要任务的SDRAM控制器的设计就变得更加重要。论文提出了一种基于状态机的SDRAM控制器的设计思路与实现,并通过了FPGA验证,完全达到系统的功能和速度要求。     

基于Q4401声码器的语音存储系统

介绍了ＶＬＳＩ声码器Ｑ４４０１的性能及特点，以及以它为核心的语音存储系统的组成及工作原理；详细分析了通过增设段地址产生电路以及设置地址表，扩大８０３１单片机寻址范围，实现８０３１单片机管理１Ｍ字节数据存储器的方法。     

相变存储器离散地址数据写入读出控制系统设计

采用传统系统受到干扰信号影响,控制能力变差,提出设计一种基于可编程逻辑阵列方式的相变存储器离散地址数据写入读出系统,可改善控制能力。选用Xilinx公司的Spartan-6芯片作为配置基础,设计抗干扰可编程逻辑阵列主—被动配置方案,促使硬件具有抵抗信号干扰的能力,以该方案对电路进行连接;将硬件主—被动配置软件功能进行设计,以时序图来展示控制结果,可抵抗外界信号造成的干扰。通过实验结果得出,该系统最低控制能力也可达到80%,即使在强信号干扰下,也能对离散地址数据写入与读出进行有效控制。     

基于S3C4510B的嵌入式系统SDRAM接口电路的实现

本文首先以HY57V641620为例重点介绍了嵌入式系统SDRAM接口电路的引脚信号及各项特性，接着详细介绍了S3C4510B系统管理器关于存储器映射的工作原理，在此基础上提出了一种基于S3C4510B的嵌入式系统SDRAM接口电路的行之有效的调试方案。并通过实验验证了该方案的正确性。     

低频低功耗高精度信号发生器的设计

本设计是基于单片机,锁相环,可编程分频,相位累加,存储器波形存储以及D/A转换器等组成的数字式函数信号发生器.输出的频率的大小由锁相环和可编程计数器来控制,最终由地址发生器对存储器中的波形数据扫描,单片机提供要输出的波形数据给存储器.这种方案电路简洁,不受单片机的时钟频率的限制,输出信号精度高,稳定性好,可靠性高,功耗低,调频、调幅都很方便,人机交互好,易于实现模块化设计.     

关于FPGA的图像采集系统的设计研究

基于FPGA图像采集技术的指导下,以FPGA为系统控制中心,连接图像传感模块、存储器SDRAM、串口UART等模块之间的逻辑运算,优化该系统的硬件设计与电路设计。以FPGA芯片设计CMOS传感器的工作方式,依据同步信号实施有效的采集,而后在FPGA内还原出原始的图像数据,以RGB的格式表现出来,并以帧为单位缓存至外部存储器SDRAM当中,最后通过UART串口与PC机连接,显示在荧幕上。该系统工作运转正常,设计方案能够实行。     

多路读写的SDRAM接口设计

介绍SDRAM的主要控制信号和基本命令时序,提出一种应用于解复用的支持多路读写的SDRAM接口设计,为需要大容量存储器的电路设计提供了新思路。     

基于SDRAM的高速大容量红外信号模拟器的实现

针对红外图像数据量大,传输速度快的特点,提出了一种以SDRAM(Synchronous Dynamic Random Access Memory,同步动态随机存储器)作为缓存器的实现方案;利用现场可编程门阵列(FPGA)作为主控芯片,解决了SDRAM的不同猝发长度读写和动态刷新等关键技术,实现了SDRAM高速接口电路;经调试,本接口电路可以按照红外探测器的时序送出图像数据,实现了4 Mbytes/s的稳定写入速度和160 Mbytes/s的峰值读出速度;实验结果表明,该方案可以满足大多数信号模拟器对容量和速度的需求.     

Improving quicksort performance with a codeword data structure

The problem is discussed of how the use of a new data structure, the codeword structure, can help improve the performance of quicksort when the records to be sorted are long and the keys are alphanumeric sequences of bytes. The codeword is a compact representation of a key with respect to some codeword generator. It consists of a byte for a character count of equal bytes, a byte for the first nonequal byte, and a pointer to the record. It is shown how the ordering of keys is preserved by an adequate choice of the code generator and how this can be applied to the quicksort algorithm. An analysis of the potential saving son various architectures and actual measurements shows the improvements that can be attained by using codewords rather than pointers. Architecturally independent parameters, such as the number of bytes to be compared, the number of swaps, architecture-dependent parameters such as caches and their write policies, and compiler optimizations such as in-line expansion and register allocation are considered     

Byte-Level Function-Associated Method for Malware Detection

The byte stream is widely used in malware detection due to its independence of reverse engineering. However, existing methods based on the byte stream implement an indiscriminate feature extraction strategy, which ignores the byte function difference in different segments and fails to achieve targeted feature extraction for various byte semantic representation modes, resulting in byte semantic confusion. To address this issue, an enhanced adversarial byte function associated method for malware backdoor attack is proposed in this paper by categorizing various function bytes into three functions involving structure, code, and data. The Minhash algorithm, grayscale mapping, and state transition probability statistics are then used to capture byte semantics from the perspectives of text signature, spatial structure, and statistical aspects, respectively, to increase the accuracy of byte semantic representation. Finally, the three-channel malware feature image is constructed based on different function byte semantics, and a convolutional neural network is applied for detection. Experiments on multiple data sets from 2018 to 2021 show that the method can effectively combine byte functions to achieve targeted feature extraction, avoid byte semantic confusion, and improve the accuracy of malware detection.     

基于2D Cache结构的H.264运动补偿访存带宽优化方法

H.264/AVC的运动补偿处理环节需要消耗大量的内存访问带宽,这成为制约其性能的关键因素.分析表明,如此巨大的带宽消耗具体来自5个方面:像素数据的重复读取、地址对齐、突发访问、SDRAM页切换和内存竞争冲突.提出一种基于2D Cache结构的运动补偿带宽优化方法,充分利用像素的重用以减少数据的重复读取.同时通过结合数据在SDRAM中映射方式的优化,将众多短而随机的访问整合为地址对齐的突发访问,并减少了访问过程中页切换的次数.此外还提出了访存的组突发访问模式,以解决SDRAM竞争冲突所引入的开销.实验结果表明采用上述优化设计后,运动补偿的访存带宽降低了82.9～87.6%,同现存优化效率较高的方法相比,带宽进一步减少了64%～87%.在达到相同带宽减少幅度的前提下,所提出的新方法比传统Cache结构电路面积减少91%.该方法目前已在一款多媒体SoC芯片设计中实际应用.     

A new content-defined chunking algorithm for data deduplication in cloud storage

Chunking is a process to split a file into smaller files called chunks. In some applications, such as remote data compression, data synchronization, and data deduplication, chunking is important because it determines the duplicate detection performance of the system. Content-defined chunking (CDC) is a method to split files into variable length chunks, where the cut points are defined by some internal features of the files. Unlike fixed-length chunks, variable-length chunks are more resistant to byte shifting. Thus, it increases the probability of finding duplicate chunks within a file and between files. However, CDC algorithms require additional computation to find the cut points which might be computationally expensive for some applications. In our previous work (Widodo et al., 2016), the hash-based CDC algorithm used in the system took more process time than other processes in the deduplication system. This paper proposes a high throughput hash-less chunking method called Rapid Asymmetric Maximum (RAM). Instead of using hashes, RAM uses bytes value to declare the cut points. The algorithm utilizes a fix-sized window and a variable-sized window to find a maximum-valued byte which is the cut point. The maximum-valued byte is included in the chunk and located at the boundary of the chunk. This configuration allows RAM to do fewer comparisons while retaining the CDC property. We compared RAM with existing hash-based and hash-less deduplication systems. The experimental results show that our proposed algorithm has higher throughput and bytes saved per second compared to other chunking algorithms.     

Rijndael-256算法的中间相遇攻击

根据Rijndael密码的算法结构,构造一个新的5轮相遇区分器：若输入状态的第一个字节可变动,而余下字节固定不变,则通过5轮加密后,算法输出的每个字节差分值均可由输入状态的第一个字节值及25个常量字节以概率2-96确定。基于该区分器,给出一种针对9轮Rijndael-256的中间相遇攻击。分析结果表明,该攻击的数据复杂度约为2128个选择明文数据量,时间复杂度约为2211.6次9轮Rijndael- 256加密。     

An artificial intelligence membrane to detect network intrusion

We propose an artificial intelligence membrane to detect network intrusion, which is analogous to a biological membrane that prevents viruses from entering cells. This artificial membrane is designed to monitor incoming packets and to prevent a malicious program code (e.g., a shellcode) from breaking into a stack or heap in a memory. While monitoring incoming TCP packets, the artificial membrane constructs a TCP segment of incoming packets, and derives the byte frequency of the TCP segment (from 0 to 255 bytes) as well as the entropy and size of the segment. These features of the segment can be classified by a data-mining technique such as a decision tree or neural network. If the data-mining method finds a suspicious byte sequence, the sequence is emulated to ensure that it is just a shellcode. If the byte sequence is a shellcode, the sequence is dropped. At the same time, an alert is communicated to the system administrator. Our experiments examined seven data-mining methods for normal and malicious network traffic. The malicious traffic included 114 shellcodes, provided by the Metasploit framework, and including 10 types of metamorphic or polymorphic shellcodes. In addition, real network traffic involving shellcodes was examined. We found that a random forest method outperformed all the other datamining methods and had a very high detection accuracy, including a true-positive rate of 99.6% and a false-positive rate of 0.4%.     

Multiplexed buses: the endian wars continue

The issue of data exchange between type-1 and type-2 buses, which multiplex the first data byte (which has the lowest address) with the least and most significant portions of the address, respectively, is considered. In an analogy based on Gulliver's Travels, the associated architectures have been dubbed little-endian and big-endian processors, respectively. It is pointed out that the byte order within integers and the byte order during transmission can differ. Therefore, the big and little adjectives are used to describe the order of bytes within integers, and the acronyms Mad and Sad to describe the order of bytes (most versus least important first) during transmission on a multiplexed address-data bus. After a review of the endian ordering issues, it is concluded that big- and little-endians can use the same bus standard. For high-performance serialized buses, the mad-endian order seems superior to a sad-endian order. For consistency between serialized and multiplexed parallel buses of various widths, the mad-endian order is proposed for future multiplexed standards. To minimize the interface costs to mad-endian buses, a big-endian order is proposed for shared data also     

一种基于特征编码技术的恶意代码检测方法

在对恶意代码进行检测和分类时,由于传统的灰度编码方法将特征转换为图像的过程中,会产生特征分裂和精度损失等问题,严重影响了恶意代码的检测性能.同时,传统的恶意代码检测和分类的数据集中只使用了单一的恶意样本,并没有考虑到良性样本.因此,文中采用了一个包含良性样本和恶意样本的数据集,同时提出了一种双字节特征编码方法.首先将待...     

Special Feature: Memory Extension Techniques for Minicomputers

There are many computer users these days who have bumped up against the end of their memories. Years ago, IBM 1800, 1130, and 7094 users discovered that 32K words of addressable memory simply was not enough. Today, minicomputer users are discovering the same thing. And so are minicomputer manufacturers. Some of the newest mini's can address 128K, 256K, 512K, or even 1024K (1M) bytes of main memory. The claim is that 16M byte addressing is right around the corner.     

11轮3D分组密码算法的中间相遇攻击

针对3D分组密码算法的安全性分析,对该算法抵抗中间相遇攻击的能力进行了评估。基于3D算法的基本结构及S盒的差分性质,减少了在构造多重集时所需的猜测字节数,从而构建了新的6轮3D算法中间相遇区分器。然后,将区分器向前扩展2轮,向后扩展3轮,得到11轮3D算法中间相遇攻击。实验结果表明:构建区分器时所需猜测的字节数为42 B,攻击时所需的数据复杂度约为2⁴⁹⁷个选择明文,时间复杂度约为2^325.3次11轮3D算法加密,存储复杂度约为2³⁴² B。新攻击表明11轮3D算法对中间相遇攻击是不免疫的。