A Scalable Counterflow-Pipelined Asynchronous Radix-4 Booth Multiplier
標(biāo)簽： Counterflow-Pipelined Asynchronous Multiplier Scalable
上傳時(shí)間： 2014-01-04
上傳用戶：jjj0202
Self timed pipelined adder
標(biāo)簽： pipelined timed adder Self
上傳時(shí)間： 2014-01-10
上傳用戶：lgnf
Computer Architecture pipelined implementation simulator
標(biāo)簽： implementation Architecture pipelined simulator
上傳時(shí)間： 2016-03-18
上傳用戶：sy_jiadeyi
On a distributed algorithm based on FPGA pipelined FIR filter of the article.
標(biāo)簽： distributed algorithm pipelined article
上傳時(shí)間： 2017-08-18
上傳用戶：liuchee
設(shè)計(jì)了一種用于高速ADC中的高速高增益的全差分CMOS運(yùn)算放大器。主運(yùn)放采用帶開關(guān)電容共模反饋的折疊式共源共柵結(jié)構(gòu)，利用增益提高和三支路電流基準(zhǔn)技術(shù)實(shí)現(xiàn)一個(gè)可用于12~14 bit精度，100 MS/s采樣頻率的高速流水線（Pipelined）ADC的運(yùn)放。設(shè)計(jì)基于SMIC 0.25 μm CMOS工藝，在Cadence環(huán)境下對(duì)電路進(jìn)行Spectre仿真。仿真結(jié)果表明，在2.5 V單電源電壓下驅(qū)動(dòng)2 pF負(fù)載時(shí)，運(yùn)放的直流增益可達(dá)到124 dB，單位增益帶寬720 MHz，轉(zhuǎn)換速率高達(dá)885 V/μs，達(dá)到0.1%的穩(wěn)定精度的建立時(shí)間只需4 ns，共模抑制比153 dB。
標(biāo)簽： CMOS 增益提高 運(yùn)算 放大器設(shè)計(jì)
上傳時(shí)間： 2014-12-23
上傳用戶：jiiszha
設(shè)計(jì)了一種用于高速ADC中的全差分套筒式運(yùn)算放大器.從ADC的應(yīng)用指標(biāo)出發(fā),確定了設(shè)計(jì)目標(biāo),利用開關(guān)電容共模反饋、增益增強(qiáng)等技術(shù)實(shí)現(xiàn)了一個(gè)可用于12 bit精度、100 MHz采樣頻率的高速流水線(Pipelined)ADC中的運(yùn)算放大器.基于SMIC 0.13 μm,3.3 V工藝,Spectre仿真結(jié)果表明,該運(yùn)放可以達(dá)到105.8 dB的增益,單位增益帶寬達(dá)到983.6 MHz,而功耗僅為26.2 mW.運(yùn)放在4 ns的時(shí)間內(nèi)可以達(dá)到0.01%的建立精度,滿足系統(tǒng)設(shè)計(jì)要求.
標(biāo)簽： 增益 增強(qiáng)型 運(yùn)算放大器
上傳時(shí)間： 2013-10-16
上傳用戶：563686540
DFT(Discrete Fourier Transformation)是數(shù)字信號(hào)分析與處理如圖形、語音及圖像等領(lǐng)域的重要變換工具，直接計(jì)算DFT的計(jì)算量與變換區(qū)間長度N的平方成正比。當(dāng)N較大時(shí)，因計(jì)算量太大，直接用DFT算法進(jìn)行譜分析和信號(hào)的實(shí)時(shí)處理是不切實(shí)際的。快速傅立葉變換(Fast Fourier Transformation，簡稱FFT)使DFT運(yùn)算效率提高1～2個(gè)數(shù)量級(jí)。其原因是當(dāng)N較大時(shí)，對(duì)DFT進(jìn)行了基4和基2分解運(yùn)算。FFT算法除了必需的數(shù)據(jù)存儲(chǔ)器ram和旋轉(zhuǎn)因子rom外，仍需較復(fù)雜的運(yùn)算和控制電路單元，即使現(xiàn)在,實(shí)現(xiàn)長點(diǎn)數(shù)的FFT仍然是很困難。本文提出的FFT實(shí)現(xiàn)算法是基于FPGA之上的，算法完成對(duì)一個(gè)序列的FFT計(jì)算，完全由脈沖觸發(fā)，外部只輸入一脈沖頭和輸入數(shù)據(jù)，便可以得到該脈沖頭作為起始標(biāo)志的N點(diǎn)FFT輸出結(jié)果。由于使用了雙ram，該算法是流型(Pipelined)的，可以連續(xù)計(jì)算N點(diǎn)復(fù)數(shù)輸入FFT，即輸入可以是分段N點(diǎn)連續(xù)復(fù)數(shù)數(shù)據(jù)流。采用DIF(Decimation In Frequency)-FFT和DIT(Decimation In Time)-FFT對(duì)于算法本身來說是無關(guān)緊要的，因?yàn)閮煞N情況下只是存儲(chǔ)器的讀寫地址有所變動(dòng)而已，不影響算法的結(jié)構(gòu)和流程，也不會(huì)對(duì)算法復(fù)雜度有何影響。
標(biāo)簽： Transformation Discrete Fourier DFT
上傳時(shí)間： 2016-04-12
上傳用戶：lx9076
VHDL implementation of the twofish cipher for 128,192 and 256 bit keys. The implementation is in library-like form All needed components up to, including the round/key schedule circuits are implemented, giving the flexibility to be combined in different architectures (iterative, rolled out/pipelined etc). Manual in English is included with more details about how to use the components and/or how to optimize some of them. All testbenches are provided (tables, variable key/text, ECB/CBC monte carlo) for 128, 192 and 256 bit key sizes, along with their respective vector files.
標(biāo)簽： implementation twofish cipher VHDL
上傳時(shí)間： 2017-06-25
上傳用戶：王小奇
In communication systems channel poses an important role. channels can convolve many different kind of distortions to our information. In perticular wireless channels multipath distortion is sevear. and more sevear is such distortion is random. To handle this, multipath affected channels require Equalizers at receaver end. such equalizer uses different learning Algorithms for identifying channels continuously. This project is VHDL implementation of LMS learning algorithm with pipelined architecture. so this implementation can work with higher data rates with less clock speed requirments and so with less power consumpiton It uses Fixed point arithmatic blocks for filtering so suitable for coustom asic.
標(biāo)簽： communication important different channels
上傳時(shí)間： 2013-12-08
上傳用戶：litianchu
The Hilbert Transform is an important component in communication systems, e.g. for single sideband modulation/demodulation, amplitude and phase detection, etc. It can be formulated as filtering operation which makes it possible to approximate the Hilbert Transform with a digital filter. Due to the non-causal and infinite impulse response of that filter, it is not that easy to get a good approximation with low hardware resource usage. Therefore, different filters with different complexities have been implemented. The detailed discussion can be found in "Digital Hilbert Transformers or FPGA-based Phase-Locked Loops" (http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4629940). The design is fully pipelined for maximum throughput.
標(biāo)簽： e.g. communication Transform important
上傳時(shí)間： 2017-06-25
上傳用戶：gxf2016
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