69 research outputs found

    PROBABILITY-DRIVEN MULTI-BIT FLIP-FLOP INTEGRATION WITH CLOCK GATING

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    Data-driven clock gated (DDCG) and multi bit flip-flops (MBFFs) are two low-power design techniques that are usually treated separately. Combining these techniques into a single grouping algorithm and design flow enables further power savings. We study MBFF multiplicity and its synergy with FF data-to-clock toggling probabilities. A probabilistic model is implemented to maximize the expected energy savings by grouping FFs in increasing order of their data-to-clock toggling probabilities. We present a front-end design flow, guided by physical layout considerations for a 65-nm 32-bit MIPS and a 28-nm industrial network processor. It is shown to achieve the power savings of 23% and 17%, respectively, compared with designs with ordinary FFs. About half of the savings was due to integrating the DDCG into the MBFFs. The proposed architecture of this paper analysis the logic size, area and power consumption using Tanner tool

    Effect of clock gating in conditional pulse enhancement flip-flop for low power applications

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    Flip-Flops (FFs) play a fundamental role in digital designs. A clock system consumes above 25% of total system power. The use of pulse-triggered flip-flops (P-FFs) in digital design provides better performance than conventional flip-flop designs. This paper presents the design of a new power-efficient implicit pulse-triggered flip-flop suitable for low power applications. This flip-flop architecture is embedded with two key features. Firstly, the enhancement in width and height of triggering pulses during specific conditions gives a solution for the longest discharging path problem in existing P-FFs. Secondly, the clock gating concept reduces unwanted switching activities at sleep/idle mode of operation and thereby reducing dynamic power consumption. The post-layout simulation results in cadence software based on CMOS 90-nm technology shows that the proposed design features less power dissipation and better power delay performance (PDP) when compared with conventional P-FFs. Its maximum power saving against conventional designs is up to 30.65%

    ์ •ํ™•ํ•˜๊ณ  ํ•™์Šต ๊ธฐ๋ฐ˜ ์ „๋ ฅ ๋ถ„์„์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜๋Š” ํด๋ก ๊ฒŒ์ดํŒ…์˜ ํ•ฉ์„ฑ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2023. 2. ๊น€ํƒœํ™˜.In this paper, we introduce two techniques to efficiently apply clock gating in the synthesis stage. First, We propose a new clock gating methodology based on a precise power saving analysis to overcome the ineffectiveness of the conventional logic structure based clock gating. Two new features exploited in our proposed clock gating are (i) the multiplexer selection signal probability that a flip-flop with multiplexer feedback loop receives a new input and (ii) the joint probability of selection signals that two flip-flops with different multiplexor selection signals both receive new inputs at the same clock cycle. In summary, our method reduces the total power consumption by 2.46% on average (up to 5.00%) over the conventional clock gating method. In the second work, we address a new problem of transforming the long toggling/untoggling sequences of flip-flops cycle-accurate activities into short embedding vectors, so that the flip-flop grouping for clock gating is practically feasible in terms of the memory usage and run time for checking activity similarity among flip-flops. To this end, we propose a machine learning based generation of embedding vectors which are accurate enough to predict the original flip-flop toggling sequences. Precisely, we develop a neural network model of LSTM (long short-term memory) based AE(autoencoder) model combined with SDAE (stacked denoising autoencoder) to take into account the time-series (i.e., clock cycle) similarity feature among the toggling sequences, which is essential to determine which flip-flops should be grouped together for clock gating. By integrating (1) our LSTM based embedding vector generation model, we propose two additional ML models for clock gating: (2) joint state probability predictor (JSP) model for generating 0-state probability of two embedding vectors, and (3) joint feature predictor (JFP) model for generating a new embedding vector that combines two embedding vectors. Through experiments, it is confirmed that our proposed LSTM combined with AutoEnc improves the toggling sequence prediction accuracy up to 0.88 while an LSTM (long short-term memory) based AE model produces accuracy to 0.72, thereby enabling our ML based clock gating framework to save the dynamic power consumption further over that by the state-of-the-art commercial clock gating tool, which relies on the flip-flops toggling probability for grouping flip-flops. Through experiments with benchmark circuits in IWLS, it is shown that our method is able to reduce the dynamic power by 14.0% on average over that by the conventional toggling-driven clock gating.๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ํ•ฉ์„ฑ ๋‹จ๊ณ„์—์„œ ํด๋ก ๊ฒŒ์ดํŒ…์„ ํšจ์œจ์ ์œผ๋กœ ์ ์šฉํ•˜๊ธฐ ์œ„ํ•œ ๋‘ ๊ฐ€์ง€ ๊ธฐ๋ฒ•์„ ์†Œ๊ฐœํ•œ๋‹ค. ์ฒซ์งธ๋กœ, ํด๋ก ๊ฒŒ์ดํŒ… ๊ธฐ๋ฐ˜์˜ ๊ธฐ์กด ๋กœ์ง ๊ตฌ์กฐ์˜ ๋น„ํšจ์œจ์„ฑ์„ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•ด ์ •๋ฐ€ ํ•œ ์ ˆ์ „ ๋ถ„์„์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ ์ƒˆ๋กœ์šด ํด๋ก ๊ฒŒ์ดํŒ… ๋ฐฉ๋ฒ•๋ก ์„ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆ๋œ ํด๋ก ๊ฒŒ์ดํŒ… ๋ฐฉ๋ฒ•์—์„œ ํ™œ์šฉ๋˜๋Š” ๋‘ ๊ฐ€์ง€ ์ƒˆ๋กœ์šด ๊ธฐ๋Šฅ์€ (i) ํ”ผ๋“œ๋ฐฑ ๋ฃจํ”„๊ฐ€ ์žˆ๋Š” ํ”Œ๋ฆฝํ”Œ๋กญ ์˜ ๋ฉ€ํ‹ฐํ”Œ๋ ‰์„œ ์„ ํƒ ์‹ ํ˜ธ ํ™•๋ฅ  ๋ฐ (ii) ์„œ๋กœ ๋‹ค๋ฅธ ๋ฉ€ํ‹ฐํ”Œ๋ ‰์„œ ์„ ํƒ ์‹ ํ˜ธ๋ฅผ ๊ฐ–๋Š” ๋‘ ํ”Œ๋ฆฝํ”Œ๋กญ์˜ ๋ฉ€ํ‹ฐํ”Œ๋ ‰์„œ ์„ ํƒ ์‹ ํ˜ธ ๊ฒฐํ•ฉ ํ™•๋ฅ ์ด๋‹ค. ์ „๋ ฅ ์ด๋“์ด ์žˆ๋Š” ๊ฒฝ์šฐ์—๋งŒ ํด๋ก ๊ฒŒ์ดํŒ…์„ ์ ์šฉํ•˜๊ณ  ์„œ๋กœ ๋‹ค๋ฅธ ํด๋ก ๊ฒŒ์ดํŒ… ๊ทธ๋ฃน์„ ํ†ตํ•ฉํ•จ์œผ๋กœ์„œ ์ „์ฒด ๋™์  ์ „๋ ฅ๋ฅผ ์ค„์ด๊ณ ์ž ํ•˜์˜€๋‹ค. ์‹คํ—˜์„ ํ†ตํ•ด ๊ธฐ์กด์˜ ํด๋ก ๊ฒŒ์ดํŒ… ๋ฐฉ๋ฒ•์— ๋น„ํ•ด ํ‰๊ท  2.46%(์ตœ๋Œ€ 5.00%)์˜ ์ด ์ „๋ ฅ ์†Œ๋น„๋ฅผ ์ค„์ด๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋‘ ๋ฒˆ์งธ๋กœ ํ”Œ๋ฆฝํ”Œ๋กญ์˜ ํด๋ก ์ฃผ๊ธฐ๋ณ„ ์ƒํƒœ๋ฅผ ๋‚˜ํƒ€๋‚ด๋Š” ๊ธด ํ† ๊ธ€๋ง/์–ธํ† ๊ธ€๋ง ์‹œํ€€์Šค ๋ฅผ ์งง์€ ์ž„๋ฒ ๋”ฉ ๋ฒกํ„ฐ๋กœ ๋ณ€ํ™˜ํ•˜๋Š” ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜์˜€๋‹ค. ์ด๋ฅผ ํ† ๊ธ€๋ง ๊ธฐ๋ฐ˜ ํด๋ก ๊ฒŒ์ด ํŒ…์„ ์œ„ํ•œ ํ”Œ๋ฆฝํ”Œ๋กญ ๊ทธ๋ฃนํ™”์— ์ ์šฉํ•˜์—ฌ ํ”Œ๋ฆฝํ”Œ๋กญ ๊ฐ„์˜ ์ƒํƒœ ์œ ์‚ฌ์„ฑ ํ™•์ธ์ด ๋ฉ”๋ชจ๋ฆฌ ์‚ฌ์šฉ๋Ÿ‰ ๋ฐ ์‹คํ–‰ ์‹œ๊ฐ„ ์ธก๋ฉด์—์„œ ์‹ค์งˆ์ ์œผ๋กœ ์‹คํ˜„ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•˜์˜€๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ๊ธฐ๊ณ„ ํ•™์Šต ๊ธฐ๋ฐ˜์œผ๋กœ ์›๋ž˜์˜ ํ”Œ๋ฆฝํ”Œ๋กญ ํ† ๊ธ€ ์‹œํ€€์Šค๋ฅผ ์˜ˆ์ธกํ•˜๊ธฐ์— ์ถฉ๋ถ„ํžˆ ์ •ํ™•ํ•œ ์ €์ฐจ์›์˜ ์ž„๋ฒ ๋”ฉ ๋ฒกํ„ฐ์˜ ์ƒ์„ฑ์„ ์ œ์•ˆํ•œ๋‹ค. ์šฐ๋ฆฌ๋Š” ํ† ๊ธ€๋ง ์‹œํ€€์Šค ๊ฐ„์˜ ์‹œ๊ณ„์—ด ์œ ์‚ฌ์„ฑ์„ ๊ณ ๋ ค ํ•˜๊ธฐ ์œ„ํ•ด ๋””๋…ธ์ด์ฆˆ ์˜คํ† ์ธ์ฝ”๋”๋ฅผ ์ด์šฉํ•˜์—ฌ 5000 ํด๋ก ์‚ฌ์ดํด์˜ ํ† ๊ธ€๋ง ์‹œํ€€์Šค๋ฅผ 10์ฐจ์›์œผ๋กœ ์••์ถ•ํ•˜๊ณ  ์ด๋ฅผ ์žฅ๋‹จ๊ธฐ ๋ฉ”๋ชจ๋ฆฌ ์˜คํ† ์ธ์ฝ”๋”์— ์ž…๋ ฅํ•˜์—ฌ ์ „์ฒด ์‹œํ€€์Šค๋ฅผ ๋Œ€๋ณ€ํ•˜๋Š” ์ €์ฐจ์› ์ž„๋ฒ ๋”ฉ ๋ฒกํ„ฐ๋ฅผ ์ƒ์„ฑํ•˜๋Š” ์‹ ๊ฒฝ๋ง ๋ชจ๋ธ์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ๋˜ํ•œ ์šฐ๋ฆฌ๋Š” ํด๋ก ๊ฒŒ์ดํŒ…์„ ์œ„ํ•œ ๋‘ ๊ฐ€์ง€ ๋ถ€๊ฐ€์ ์ธ ์‹ ๊ฒฝ๋ง ๋ชจ๋ธ์ธ (1) 2๊ฐœ์˜ ์ž„๋ฒ ๋”ฉ ๋ฒกํ„ฐ์˜ 0- ์ƒํƒœ ํ™•๋ฅ  ์ƒ์„ฑ์„ ์œ„ํ•œ ๊ฒฐํ•ฉ ํ™•๋ฅ  ์˜ˆ์ธก ๋ชจ๋ธ๊ณผ (2) ๋‘ ๊ฐœ์˜ ์ž„๋ฒ ๋”ฉ ๋ฒกํ„ฐ๋ฅผ ๊ฒฐํ•ฉํ•˜์—ฌ ์ƒˆ๋กœ์šด ์ž„๋ฒ ๋”ฉ ๋ฒกํ„ฐ๋ฅผ ์˜ˆ์ธกํ•˜๋Š” ๊ฒฐํ•ฉ ํŠน์ง• ์˜ˆ์ธก ๋ชจ๋ธ์„ ์ œ์•ˆํ•œ๋‹ค. IWLS ๋ฒค์น˜๋งˆํฌ ํšŒ๋กœ๋ฅผ ์ด์šฉํ•œ ์‹คํ—˜์„ ํ†ตํ•ด, ๋””๋…ธ์ด์ฆˆ ์˜คํ† ์ธ์ฝ”๋”๋งŒ ์‚ฌ์šฉํ–ˆ์„๋•Œ๋ณด๋‹ค ์žฅ๋‹จ๊ธฐ ๋ฉ”๋ชจ๋ฆฌ ๊ธฐ๋ฐ˜์˜ ์˜คํ† ์ธ์ฝ”๋”๋ฅผ ๊ฒฐํ•ฉํ–ˆ์„ ๋•Œ ์ž…๋ ฅ ๋ฐ์ดํ„ฐ๋ฅผ ๋ณต์› ์ •ํ™•๋„๊ฐ€ ๋” ์šฐ์ˆ˜ํ•œ ๊ฒƒ์„ ํ™• ์ธํ•˜์˜€๋‹ค. ๋˜ํ•œ ์šฐ๋ฆฌ์˜ ๋ฐฉ๋ฒ•์ด ๊ธฐ์กด์˜ ํ† ๊ธ€๋ง ๊ธฐ๋ฐ˜ ํด๋ก ๊ฒŒ์ดํŒ…์— ๋น„ํ•ด ํ‰๊ท  14.0% ์˜ ๋™์  ์ „๋ ฅ์„ ์ค„์ผ ์ˆ˜ ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค.1 Selective Clock Gating Based on Comprehensive Power Saving Analysis 1 1.1 Introduction 1 1.2 Preliminary and Motivation 1 1.3 Selective Clock Gating 3 1.3.1 Concept of Selective Clock Gating 3 1.3.2 Joint probability of selection signals 5 1.4 Experimental Results 6 1.4.1 Experimental Setup 6 1.4.2 Experimental Result 7 1.5 Conclusion 10 2 Machine Learning Based Flip-Flop Grouping for Toggling Driven Clock Gating 11 2.1 Introduction 11 2.2 Preliminaries and Prior Works 13 2.2.1 Preliminary and Motivation 13 2.2.2 Prior Works 14 2.3 Machine Learning Based Clock Gating Framework 14 2.3.1 Primary Model: Embedding Vector Generation 14 2.3.2 Secondary Models: Joint State Probability and Joint Feature Prediction 17 2.3.3 Distance Analysis Between Embedding Vectors 18 2.3.4 Power Analysis Model 19 2.3.5 Overall Flow of Flip-flop Grouping 19 2.4 Experimental Results 19 2.4.1 Comparison of Dynamic Power Saving 20 2.4.2 Performance of Auto-encoder Reconstruction Model 21 2.5 Conclusion 21 Abstract (In Korean) 26์„

    ์ •์  ๋žจ ๋ฐ ํŒŒ์›Œ ๊ฒŒ์ดํŠธ ํšŒ๋กœ์— ๋Œ€ํ•œ ์ „์•• ๋ฐ ๋ณด์กด์šฉ ๊ณต๊ฐ„ ํ• ๋‹น ๋ฌธ์ œ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2021.8. ๊น€ํƒœํ™˜.์นฉ์˜ ์ €์ „๋ ฅ ๋™์ž‘์€ ์ค‘์š”ํ•œ ๋ฌธ์ œ์ด๋ฉฐ, ๊ณต์ •์ด ๋ฐœ์ „ํ•˜๋ฉด์„œ ๊ทธ ์ค‘์š”์„ฑ์€ ์ ์  ์ปค์ง€๊ณ  ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ์นฉ์„ ๊ตฌ์„ฑํ•˜๋Š” ์ •์  ๋žจ(SRAM) ๋ฐ ๋กœ์ง(logic) ๊ฐ๊ฐ์— ๋Œ€ํ•ด์„œ ์ €์ „๋ ฅ์œผ๋กœ ๋™์ž‘์‹œํ‚ค๋Š” ๋ฐฉ๋ฒ•๋ก ์„ ๋…ผํ•œ๋‹ค. ์šฐ์„ , ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์นฉ์„ ๋ฌธํ„ฑ ์ „์•• ๊ทผ์ฒ˜์˜ ์ „์••(NTV)์—์„œ ๋™์ž‘์‹œํ‚ค๊ณ ์ž ํ•  ๋•Œ ๋ชจ๋‹ˆํ„ฐ๋ง ํšŒ๋กœ์˜ ์ธก์ •์„ ํ†ตํ•ด ์นฉ ๋‚ด์˜ ๋ชจ๋“  SRAM ๋ธ”๋ก์—์„œ ๋™์ž‘ ์‹คํŒจ๊ฐ€ ๋ฐœ์ƒํ•˜์ง€ ์•Š๋Š” ์ตœ์†Œ ๋™์ž‘ ์ „์••์„ ์ถ”๋ก ํ•˜๋Š” ๋ฐฉ๋ฒ•๋ก ์„ ์ œ์•ˆํ•œ๋‹ค. ์นฉ์„ NTV ์˜์—ญ์—์„œ ๋™์ž‘์‹œํ‚ค๋Š” ๊ฒƒ์€ ์—๋„ˆ์ง€ ํšจ์œจ์„ฑ์„ ์ฆ๋Œ€์‹œํ‚ฌ ์ˆ˜ ์žˆ๋Š” ๋งค์šฐ ํšจ๊ณผ์ ์ธ ๋ฐฉ๋ฒ• ์ค‘ ํ•˜๋‚˜์ด์ง€๋งŒ SRAM์˜ ๊ฒฝ์šฐ ๋™์ž‘ ์‹คํŒจ ๋•Œ๋ฌธ์— ๋™์ž‘ ์ „์••์„ ๋‚ฎ์ถ”๊ธฐ ์–ด๋ ต๋‹ค. ํ•˜์ง€๋งŒ ์นฉ๋งˆ๋‹ค ์˜ํ–ฅ์„ ๋ฐ›๋Š” ๊ณต์ • ๋ณ€์ด๊ฐ€ ๋‹ค๋ฅด๋ฏ€๋กœ ์ตœ์†Œ ๋™์ž‘ ์ „์••์€ ์นฉ๋งˆ๋‹ค ๋‹ค๋ฅด๋ฉฐ, ๋ชจ๋‹ˆํ„ฐ๋ง์„ ํ†ตํ•ด ์ด๋ฅผ ์ถ”๋ก ํ•ด๋‚ผ ์ˆ˜ ์žˆ๋‹ค๋ฉด ์นฉ๋ณ„๋กœ SRAM์— ์„œ๋กœ ๋‹ค๋ฅธ ์ „์••์„ ์ธ๊ฐ€ํ•ด ์—๋„ˆ์ง€ ํšจ์œจ์„ฑ์„ ๋†’์ผ ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™์€ ๊ณผ์ •์„ ํ†ตํ•ด ์ด ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•œ๋‹ค: (1) ๋””์ž์ธ ์ธํ”„๋ผ ์„ค๊ณ„ ๋‹จ๊ณ„์—์„œ๋Š” SRAM์˜ ์ตœ์†Œ ๋™์ž‘ ์ „์••์„ ์ถ”๋ก ํ•˜๊ณ  ์นฉ ์ƒ์‚ฐ ๋‹จ๊ณ„์—์„œ๋Š” SRAM ๋ชจ๋‹ˆํ„ฐ์˜ ์ธก์ •์„ ํ†ตํ•ด ์ „์••์„ ์ธ๊ฐ€ํ•˜๋Š” ๋ฐฉ๋ฒ•๋ก ์„ ์ œ์•ˆํ•œ๋‹ค; (2) ์นฉ์˜ SRAM ๋น„ํŠธ์…€(bitcell)๊ณผ ์ฃผ๋ณ€ ํšŒ๋กœ๋ฅผ ํฌํ•จํ•œ SRAM ๋ธ”๋ก๋“ค์˜ ๊ณต์ • ๋ณ€์ด๋ฅผ ๋ชจ๋‹ˆํ„ฐ๋งํ•  ์ˆ˜ ์žˆ๋Š” SRAM ๋ชจ๋‹ˆํ„ฐ์™€ SRAM ๋ชจ๋‹ˆํ„ฐ์—์„œ ๋ชจ๋‹ˆํ„ฐ๋งํ•  ๋Œ€์ƒ์„ ์ •์˜ํ•œ๋‹ค; (3) SRAM ๋ชจ๋‹ˆํ„ฐ์˜ ์ธก์ •๊ฐ’์„ ์ด์šฉํ•ด ๊ฐ™์€ ์นฉ์— ์กด์žฌํ•˜๋Š” ๋ชจ๋“  SRAM ๋ธ”๋ก์—์„œ ๋ชฉํ‘œ ์‹ ๋ขฐ์ˆ˜์ค€ ๋‚ด์—์„œ ์ฝ๊ธฐ, ์“ฐ๊ธฐ, ๋ฐ ์ ‘๊ทผ ๋™์ž‘ ์‹คํŒจ๊ฐ€ ๋ฐœ์ƒํ•˜์ง€ ์•Š๋Š” ์ตœ์†Œ ๋™์ž‘ ์ „์••์„ ์ถ”๋ก ํ•œ๋‹ค. ๋ฒค์น˜๋งˆํฌ ํšŒ๋กœ์˜ ์‹คํ—˜ ๊ฒฐ๊ณผ๋Š” ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ œ์•ˆํ•œ ๋ฐฉ๋ฒ•์„ ๋”ฐ๋ผ ์นฉ๋ณ„๋กœ SRAM ๋ธ”๋ก๋“ค์˜ ์ตœ์†Œ ๋™์ž‘ ์ „์••์„ ๋‹ค๋ฅด๊ฒŒ ์ธ๊ฐ€ํ•  ๊ฒฝ์šฐ, ๊ธฐ์กด ๋ฐฉ๋ฒ•๋Œ€๋กœ ๋ชจ๋“  ์นฉ์— ๋™์ผํ•œ ์ „์••์„ ์ธ๊ฐ€ํ•˜๋Š” ๊ฒƒ ๋Œ€๋น„ ์ˆ˜์œจ์€ ๊ฐ™์€ ์ˆ˜์ค€์œผ๋กœ ์œ ์ง€ํ•˜๋ฉด์„œ SRAM ๋น„ํŠธ์…€ ๋ฐฐ์—ด์˜ ์ „๋ ฅ ์†Œ๋ชจ๋ฅผ ๊ฐ์†Œ์‹œํ‚ฌ ์ˆ˜ ์žˆ์Œ์„ ๋ณด์ธ๋‹ค. ๋‘ ๋ฒˆ์งธ๋กœ, ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ํŒŒ์›Œ ๊ฒŒ์ดํŠธ ํšŒ๋กœ์—์„œ ๊ธฐ์กด์˜ ๋ณด์กด์šฉ ๊ณต๊ฐ„ ํ• ๋‹น ๋ฐฉ๋ฒ•๋“ค์ด ์ง€๋‹ˆ๊ณ  ์žˆ๋Š” ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ณ  ๋ˆ„์„ค ์ „๋ ฅ ์†Œ๋ชจ๋ฅผ ๋” ์ค„์ผ ์ˆ˜ ์žˆ๋Š” ๋ฐฉ๋ฒ•๋ก ์„ ์ œ์•ˆํ•œ๋‹ค. ๊ธฐ์กด์˜ ๋ณด์กด์šฉ ๊ณต๊ฐ„ ํ• ๋‹น ๋ฐฉ๋ฒ•์€ ๋ฉ€ํ‹ฐํ”Œ๋ ‰์„œ ํ”ผ๋“œ๋ฐฑ ๋ฃจํ”„๊ฐ€ ์žˆ๋Š” ๋ชจ๋“  ํ”Œ๋ฆฝํ”Œ๋กญ์—๋Š” ๋ฌด์กฐ๊ฑด ๋ณด์กด์šฉ ๊ณต๊ฐ„์„ ํ• ๋‹นํ•ด์•ผ ํ•ด์•ผ ํ•˜๊ธฐ ๋•Œ๋ฌธ์— ๋‹ค์ค‘ ๋น„ํŠธ ๋ณด์กด์šฉ ๊ณต๊ฐ„์˜ ์žฅ์ ์„ ์ถฉ๋ถ„ํžˆ ์‚ด๋ฆฌ์ง€ ๋ชปํ•˜๋Š” ๋ฌธ์ œ๊ฐ€ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™์€ ๋ฐฉ๋ฒ•์„ ํ†ตํ•ด ๋ณด์กด์šฉ ๊ณต๊ฐ„์„ ์ตœ์†Œํ™”ํ•˜๋Š” ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•œ๋‹ค: (1) ๋ณด์กด์šฉ ๊ณต๊ฐ„ ํ• ๋‹น ๊ณผ์ •์—์„œ ๋ฉ€ํ‹ฐํ”Œ๋ ‰์„œ ํ”ผ๋“œ๋ฐฑ ๋ฃจํ”„๋ฅผ ๋ฌด์‹œํ•  ์ˆ˜ ์žˆ๋Š” ์กฐ๊ฑด์„ ์ œ์‹œํ•˜๊ณ , (2) ํ•ด๋‹น ์กฐ๊ฑด์„ ์ด์šฉํ•ด ๋ฉ€ํ‹ฐํ”Œ๋ ‰์„œ ํ”ผ๋“œ๋ฐฑ ๋ฃจํ”„๊ฐ€ ์žˆ๋Š” ํ”Œ๋ฆฝํ”Œ๋กญ์ด ๋งŽ์ด ์กด์žฌํ•˜๋Š” ํšŒ๋กœ์—์„œ ๋ณด์กด์šฉ ๊ณต๊ฐ„์„ ์ตœ์†Œํ™”ํ•œ๋‹ค; (3) ์ถ”๊ฐ€๋กœ, ํ”Œ๋ฆฝํ”Œ๋กญ์— ์ด๋ฏธ ํ• ๋‹น๋œ ๋ณด์กด์šฉ ๊ณต๊ฐ„ ์ค‘ ์ผ๋ถ€๋ฅผ ์ œ๊ฑฐํ•  ์ˆ˜ ์žˆ๋Š” ์กฐ๊ฑด์„ ์ฐพ๊ณ , ์ด๋ฅผ ์ด์šฉํ•ด ๋ณด์กด์šฉ ๊ณต๊ฐ„์„ ๋” ๊ฐ์†Œ์‹œํ‚จ๋‹ค. ๋ฒค์น˜๋งˆํฌ ํšŒ๋กœ์˜ ์‹คํ—˜ ๊ฒฐ๊ณผ๋Š” ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ œ์•ˆํ•œ ๋ฐฉ๋ฒ•๋ก ์ด ๊ธฐ์กด์˜ ๋ณด์กด์šฉ ๊ณต๊ฐ„ ํ• ๋‹น ๋ฐฉ๋ฒ•๋ก ๋ณด๋‹ค ๋” ์ ์€ ๋ณด์กด์šฉ ๊ณต๊ฐ„์„ ํ• ๋‹นํ•˜๋ฉฐ, ๋”ฐ๋ผ์„œ ์นฉ์˜ ๋ฉด์  ๋ฐ ์ „๋ ฅ ์†Œ๋ชจ๋ฅผ ๊ฐ์†Œ์‹œํ‚ฌ ์ˆ˜ ์žˆ์Œ์„ ๋ณด์ธ๋‹ค.Low power operation of a chip is an important issue, and its importance is increasing as the process technology advances. This dissertation addresses the methodology of operating at low power for each of the SRAM and logic constituting the chip. Firstly, we propose a methodology to infer the minimum operating voltage at which SRAM failure does not occur in all SRAM blocks in the chip operating on near threshold voltage (NTV) regime through the measurement of a monitoring circuit. Operating the chip on NTV regime is one of the most effective ways to increase energy efficiency, but in case of SRAM, it is difficult to lower the operating voltage because of SRAM failure. However, since the process variation on each chip is different, the minimum operating voltage is also different for each chip. If it is possible to infer the minimum operating voltage of SRAM blocks of each chip through monitoring, energy efficiency can be increased by applying different voltage. In this dissertation, we propose a new methodology of resolving this problem. Specifically, (1) we propose to infer minimum operation voltage of SRAM in design infra development phase, and assign the voltage using measurement of SRAM monitor in silicon production phase; (2) we define a SRAM monitor and features to be monitored that can monitor process variation on SRAM blocks including SRAM bitcell and peripheral circuits; (3) we propose a new methodology of inferring minimum operating voltage of SRAM blocks in a chip that does not cause read, write, and access failures under a target confidence level. Through experiments with benchmark circuits, it is confirmed that applying different voltage to SRAM blocks in each chip that inferred by our proposed methodology can save overall power consumption of SRAM bitcell array compared to applying same voltage to SRAM blocks in all chips, while meeting the same yield target. Secondly, we propose a methodology to resolve the problem of the conventional retention storage allocation methods and thereby further reduce leakage power consumption of power gated circuit. Conventional retention storage allocation methods have problem of not fully utilizing the advantage of multi-bit retention storage because of the unavoidable allocation of retention storage on flip-flops with mux-feedback loop. In this dissertation, we propose a new methodology of breaking the bottleneck of minimizing the state retention storage. Specifically, (1) we find a condition that mux-feedback loop can be disregarded during the retention storage allocation; (2) utilizing the condition, we minimize the retention storage of circuits that contain many flip-flops with mux-feedback loop; (3) we find a condition to remove some of the retention storage already allocated to each of flip-flops and propose to further reduce the retention storage. Through experiments with benchmark circuits, it is confirmed that our proposed methodology allocates less retention storage compared to the state-of-the-art methods, occupying less cell area and consuming less power.1 Introduction 1 1.1 Low Voltage SRAM Monitoring Methodology 1 1.2 Retention Storage Allocation on Power Gated Circuit 5 1.3 Contributions of this Dissertation 8 2 SRAM On-Chip Monitoring Methodology for High Yield and Energy Efficient Memory Operation at Near Threshold Voltage 13 2.1 SRAM Failures 13 2.1.1 Read Failure 13 2.1.2 Write Failure 15 2.1.3 Access Failure 16 2.1.4 Hold Failure 16 2.2 SRAM On-chip Monitoring Methodology: Bitcell Variation 18 2.2.1 Overall Flow 18 2.2.2 SRAM Monitor and Monitoring Target 18 2.2.3 Vfail to Vddmin Inference 22 2.3 SRAM On-chip Monitoring Methodology: Peripheral Circuit IR Drop and Variation 29 2.3.1 Consideration of IR Drop 29 2.3.2 Consideration of Peripheral Circuit Variation 30 2.3.3 Vddmin Prediction including Access Failure Prohibition 33 2.4 Experimental Results 41 2.4.1 Vddmin Considering Read and Write Failures 42 2.4.2 Vddmin Considering Read/Write and Access Failures 45 2.4.3 Observation for Practical Use 45 3 Allocation of Always-On State Retention Storage for Power Gated Circuits - Steady State Driven Approach 49 3.1 Motivations and Analysis 49 3.1.1 Impact of Self-loop on Power Gating 49 3.1.2 Circuit Behavior Before Sleeping 52 3.1.3 Wakeup Latency vs. Retention Storage 54 3.2 Steady State Driven Retention Storage Allocation 56 3.2.1 Extracting Steady State Self-loop FFs 57 3.2.2 Allocating State Retention Storage 59 3.2.3 Designing and Optimizing Steady State Monitoring Logic 59 3.2.4 Analysis of the Impact of Steady State Monitoring Time on the Standby Power 63 3.3 Retention Storage Refinement Utilizing Steadiness 65 3.3.1 Extracting Flip-flops for Retention Storage Refinement 66 3.3.2 Designing State Monitoring Logic and Control Signals 68 3.4 Experimental Results 73 3.4.1 Comparison of State Retention Storage 75 3.4.2 Comparison of Power Consumption 79 3.4.3 Impact on Circuit Performance 82 3.4.4 Support for Immediate Power Gating 83 4 Conclusions 89 4.1 Chapter 2 89 4.2 Chapter 3 90๋ฐ•

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€,2020. 2. ๊น€ํƒœํ™˜.์ €์ „๋ ฅ ์„ค๊ณ„๋Š” ์ตœ์‹  ์‹œ์Šคํ…œ-์˜จ-์นฉ (SoCs) ์„ค๊ณ„์—์„œ ๋งค์šฐ ์ค‘์š”ํ•œ ์š”์†Œ ์ค‘์˜ ํ•˜๋‚˜์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋™์  ๋ฐ ์ •์  ์ „๋ ฅ ์†Œ๋น„๋ฅผ ๊ฐ์†Œ์‹œํ‚ค๊ธฐ ์œ„ํ•œ ์ €์ „๋ ฅ ์„ค๊ณ„ ๋ฐฉ๋ฒ•๋ก ์— ๋Œ€ํ•ด ๋…ผํ•œ๋‹ค. ๊ตฌ์ฒด์ ์œผ๋กœ ๋น„์šฉ ํšจ์œจ์ ์ธ ์ €์ „๋ ฅ ์„ค๊ณ„๋ฅผ ์œ„ํ•˜์—ฌ ๋‘ ๊ฐ€์ง€ ์ƒˆ๋กœ์šด ๊ธฐ์ˆ ์„ ์ œ์•ˆํ•œ๋‹ค. ์šฐ์„  ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋™์  ์ „๋ ฅ ์†Œ๋น„๋ฅผ ์ค„์ผ ์ˆ˜ ์žˆ๋Š” ์ƒˆ๋กœ์šด ํด๋Ÿญ ๊ฒŒ์ดํŒ… ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ๊ธฐ์กด ํ”Œ๋ฆฝ-ํ”Œ๋ž ์ž…๋ ฅ ๋ฐ์ดํ„ฐ ํ† ๊ธ€ ๊ธฐ๋ฐ˜ ํด๋Ÿญ ๊ฒŒ์ดํŒ…์€ ๊ฐ€์žฅ ๋„๋ฆฌ ์‚ฌ์šฉ๋˜๋Š” ํด๋Ÿญ ๊ฒŒ์ดํŒ… ๊ธฐ๋ฒ• ์ค‘์˜ ํ•˜๋‚˜์ด๋‹ค. ํ•˜์ง€๋งŒ ์ด ๋ฐฉ๋ฒ•์€ ๋” ๋งŽ์€ ํ”Œ๋ฆฝ-ํ”Œ๋ž์— ๋Œ€ํ•ด ์ ์šฉํ• ์ˆ˜๋ก ํด๋Ÿญ ๊ฒŒ์ดํŒ…์— ํ•„์š”ํ•œ ๋ถ€๊ฐ€ ํšŒ๋กœ๊ฐ€ ๊ธ‰๊ฒฉํžˆ ์ฆ๊ฐ€ํ•œ๋‹ค๋Š” ๊ทผ๋ณธ์ ์ธ ํ•œ๊ณ„๋ฅผ ์ง€๋‹ˆ๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™์ด ์ƒˆ๋กœ์šด ํด๋Ÿญ ๊ฒŒ์ดํŒ… ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ฒซ ๋ฒˆ์งธ๋กœ ๊ธฐ์กด ์ž…๋ ฅ ๋ฐ์ดํ„ฐ ํ† ๊ธ€ ๊ธฐ๋ฐ˜ ํด๋Ÿญ ๊ฒŒ์ดํŒ… ๋ฐฉ๋ฒ•์— ํ•„์š”ํ•œ ํšŒ๋กœ ์ž์›์„ ๋ถ„์„ํ•˜์—ฌ ํ•ด๋‹น ๋ฐฉ๋ฒ•์˜ ๋น„ํšจ์œจ์„ฑ์„ ๋ณด์ด๊ณ , ๊ธฐ์กด ๋ฐฉ๋ฒ•์—์„œ ์‚ฌ์šฉ๋˜๋Š” ์ž…๋ ฅ ๋ฐ์ดํ„ฐ ํ† ๊ธ€ ๊ฒ€์ถœ์— ํ•„์ˆ˜์ ์ด์ง€๋งŒ ๊ณ ๋น„์šฉ์˜ XOR ๊ฒŒ์ดํŠธ๋ฅผ ์™„๋ฒฝํžˆ ์ œ๊ฑฐํ•œ ํ”Œ๋ฆฝ-ํ”Œ๋ž ์ƒํƒœ ๊ธฐ๋ฐ˜ ํด๋Ÿญ ๊ฒŒ์ดํŒ…'์ด๋ผ๋Š” ์ƒˆ๋กœ์šด ํด๋Ÿญ ๊ฒŒ์ดํŒ… ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ๋‘ ๋ฒˆ์งธ๋กœ ์ œ์•ˆ๋œ XOR ๊ฒŒ์ดํŠธ๊ฐ€ ํ•„์š” ์—†๋Š” ํด๋Ÿญ ๊ฒŒ์ดํŒ… ๋ฐฉ๋ฒ•์„ ์œ„ํ•œ ๋ถ€๊ฐ€ ํšŒ๋กœ๋ฅผ ์ œ์‹œํ•˜๋ฉฐ, ๋‹ค์–‘ํ•œ ํƒ€์ด๋ฐ ๋ถ„์„์„ ํ†ตํ•˜์—ฌ ํ•ด๋‹น ํšŒ๋กœ๊ฐ€ ์•ˆ์ •์ ์œผ๋กœ ์ ์šฉ๋  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์ธ๋‹ค. ์„ธ ๋ฒˆ์งธ๋กœ ํšŒ๋กœ์˜ ํ”Œ๋ฆฝ-ํ”Œ๋ž ์ƒํƒœ ํ”„๋กœํŒŒ์ผ์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ, ์ œ์•ˆ๋œ ํด๋Ÿญ ๊ฒŒ์ดํŒ… ๊ธฐ๋ฒ•์„ ๊ธฐ์กด ํด๋Ÿญ ๊ฒŒ์ดํŒ… ๊ธฐ๋ฒ•๊ณผ ์™„๋ฒฝํ•˜๊ฒŒ ํ†ตํ•ฉํ•  ์ˆ˜ ์žˆ๋Š” ํด๋Ÿญ ๊ฒŒ์ดํŒ… ๋ฐฉ๋ฒ•๋ก ์„ ์ œ์•ˆํ•œ๋‹ค. ์—ฌ๋Ÿฌ ๋ฒค์น˜๋งˆํฌ ํšŒ๋กœ์— ๋Œ€ํ•œ ์‹คํ—˜ ๊ฒฐ๊ณผ๋Š” ๊ธฐ์กด ์ž…๋ ฅ ๋ฐ์ดํ„ฐ ํ† ๊ธ€ ๊ธฐ๋ฐ˜ ํด๋Ÿญ ๊ฒŒ์ดํŒ… ๋ฐฉ๋ฒ•์ด ์ „๋ ฅ ์†Œ๋น„ ์ ˆ๊ฐ ๊ธฐํšŒ๋ฅผ ๋†“์น˜๋Š” ๋ฐ˜๋ฉด ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•์€ ๋ชจ๋“  ํƒ€์ด๋ฐ ์ œ์•ฝ ์กฐ๊ฑด์„ ๋งŒ์กฑํ•˜๋ฉด์„œ ์ „๋ ฅ ์†Œ๋น„ ๊ฐ์†Œ์— ๋งค์šฐ ํšจ๊ณผ์ ์ž„์„ ๋ณด์—ฌ์ค€๋‹ค. ๋‹ค์Œ์œผ๋กœ ์ •์  ์ „๋ ฅ ์†Œ๋น„๋ฅผ ์ค„์ด๊ธฐ ์œ„ํ•œ ๋ฐฉ์•ˆ์œผ๋กœ, ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๊ธฐ์กด ํŒŒ์›Œ ๊ฒŒ์ดํŠธ ํšŒ๋กœ์˜ ์ƒํƒœ ๋ณด์กด์šฉ ์ €์žฅ ๊ณต๊ฐ„ ํ• ๋‹น ๋ฐฉ๋ฒ•๋“ค์ด ์ง€๋‹ˆ๊ณ  ์žˆ๋Š” ๋‘ ๊ฐ€์ง€ ์ค‘์š”ํ•œ ํ•œ๊ณ„๋“ค์„ ํ•ด๊ฒฐํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ค‘์š”ํ•œ ํ•œ๊ณ„๋“ค์ด๋ž€ ์ฒซ ๋ฒˆ์งธ๋กœ ๋‹ค์ค‘-๋น„ํŠธ ์ƒํƒœ ๋ณด์กด ํ”Œ๋ฆฝ-ํ”Œ๋ž์˜ ๋ฌด๋ถ„๋ณ„ํ•œ ์‚ฌ์šฉ์œผ๋กœ ์ธํ•œ ๊ธด ์›จ์ดํฌ์—… ์ง€์—ฐ ์‹œ๊ฐ„์ด๋ฉฐ, ๋‘ ๋ฒˆ์งธ๋กœ ๋ฉ€ํ‹ฐํ”Œ๋ ‰์„œ ๋˜๋จน์ž„ ๋ฃจํ”„๊ฐ€ ์žˆ๋Š” ์ƒํƒœ ๋ณด์กด ํ”Œ๋ฆฝ-ํ”Œ๋ž์˜ ์ตœ์ ํ™” ๋ถˆ๊ฐ€๋Šฅ์„ฑ์ด๋‹ค. ๊ธฐ์กด ๋ฐฉ๋ฒ•๋“ค์—์„œ๋Š” ์ƒํƒœ ๋ณด์กด์„ ์œ„ํ•œ ์ €์žฅ ๊ณต๊ฐ„์„ ์ตœ์†Œํ™”ํ•˜๊ธฐ ์œ„ํ•ด ๊ธด ์›จ์ดํฌ์—… ์ง€์—ฐ ์‹œ๊ฐ„์ด ํ•„์ˆ˜์ ์ด์—ˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๋˜๋จน์ž„ ๋ฃจํ”„๊ฐ€ ์žˆ๋Š” ํ”Œ๋ฆฝ-ํ”Œ๋ž์€ ์ตœ์ ํ™”ํ•  ์ˆ˜ ์—†๋Š” ๋Œ€์ƒ์œผ๋กœ ๋‹ค๋ฃจ์–ด์กŒ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ผ๋ฐ˜์ ์œผ๋กœ ํ•˜๋“œ์›จ์–ด ๊ธฐ์ˆ  ์–ธ์–ด(HDL)๋กœ๋ถ€ํ„ฐ ์ƒ์„ฑ๋˜๋Š” ๋˜๋จน์ž„ ๋ฃจํ”„๋ฅผ ์ง€๋‹Œ ํ”Œ๋ฆฝ-ํ”Œ๋ž์€ ๋ฌด์‹œํ•  ์ˆ˜ ์žˆ์„ ์ •๋„๋กœ ์ ์€ ์–‘์ด ์•„๋‹ˆ๋‹ค. ์ฒซ ๋ฒˆ์งธ ํ•œ๊ณ„๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•œ ๋ฐฉ๋ฒ•์œผ๋กœ ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ตœ๋Œ€ 2 ๋น„ํŠธ์˜ ๋‹ค์ค‘-๋น„ํŠธ ์ƒํƒœ ๋ณด์กด ํ”Œ๋ฆฝ-ํ”Œ๋ž์„ ์‚ฌ์šฉํ•˜์—ฌ ์›จ์ดํฌ์—… ์ง€์—ฐ ์‹œ๊ฐ„์„ ๋‘ ํด๋Ÿญ ์‚ฌ์ดํด๋กœ ์ œํ•œํ•˜๋ฉด์„œ๋„ ์ƒํƒœ ๋ณด์กด์„ ์œ„ํ•œ ์ €์žฅ ๊ณต๊ฐ„์„ ํšจ์œจ์ ์œผ๋กœ ์ ˆ์•ฝํ•  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์ธ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๋‘ ๋ฒˆ์งธ ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•ด์„œ ๋˜๋จน์ž„ ๋ฃจํ”„๋ฅผ ์ง€๋‹Œ ํ”Œ๋ฆฝ-ํ”Œ๋ž์ด ํฌํ•จ๋œ ๋‘ ํ”Œ๋ฆฝ-ํ”Œ๋ž ์Œ์˜ ์ƒํƒœ๋ฅผ ๋ณต์›ํ•  ์ˆ˜ ์žˆ๋Š” 2๋‹จ ์ƒํƒœ ๋ณด์กด ์ œ์–ด ๋ฐฉ์•ˆ์„ ์ œ์•ˆํ•œ๋‹ค. ๋˜ํ•œ ์ฃผ์–ด์ง„ ํšŒ๋กœ์—์„œ ์ถฉ๋Œ์—†์ด ๋™์‹œ์— ์กด์žฌํ•  ์ˆ˜ ์žˆ๋Š” ํ”Œ๋ฆฝ-ํ”Œ๋ž ์Œ์„ ์ตœ๋Œ€๋กœ ์ถ”์ถœํ•˜๊ธฐ ์œ„ํ•ด ๋…๋ฆฝ ์ง‘ํ•ฉ ๋ฌธ์ œ(independent set problem)๊ธฐ๋ฐ˜์˜ ์—ฐ์‚ฐ๋ฒ•๋„ ์ œ์•ˆํ•œ๋‹ค. ๋ฒค์น˜๋งˆํฌ ํšŒ๋กœ์— ๋Œ€ํ•œ ์‹คํ—˜ ๊ฒฐ๊ณผ๋Š” ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•์ด ์›จ์ดํฌ์—… ์ง€์—ฐ ์‹œ๊ฐ„์„ ๋‘ ํด๋Ÿญ ์‚ฌ์ดํด๋กœ ์ œํ•œํ•˜๋ฉด์„œ๋„ ์ƒํƒœ ๋ณด์กด์— ํ•„์š”ํ•œ ์ €์žฅ ๊ณต๊ฐ„๊ณผ ํŒŒ์›Œ๋ฅผ ๊ฐ์†Œ์‹œํ‚ค๋Š”๋ฐ ๋งค์šฐ ํšจ๊ณผ์ ์ž„์„ ๋ณด์—ฌ์ค€๋‹ค.Low power design is of great importance in modern system-on-chips (SoCs). This dissertation studies on low power design methodologies for saving dynamic and static power consumption. Precisely, we unveil two novel techniques of cost effective low power design. Firstly, we propose a novel clock gating method for reducing the dynamic power consumption. Flip-flop's input data toggling based clock gating is one of the most commonly used clock gating methods, in which one critical and inherent limitation is the sharp increase of gating logic as more flip-flops are involved in gating. In this dissertation, we propose a new clock gating method to overcome this limitation. Specifically, (1) we analyze the resources of gating logic in the input data toggling based clock gating, from which an ineffectiveness in resource utilization is observed and we propose a new clock gating technique called flip-flop state driven clock gating which completely eliminates the essential and expensive component of XOR gates for detecting input toggling of flip-flops; (2) we provide the supporting logic circuitry of our proposed XOR-free clock gating, confirming its safe applicability through a comprehensive timing analysis; (3) we propose, based on the flip-flops' state profile, a clock gating methodology that seamlessly combines our flip-flop state based clock gating with the toggling based clock gating. Through experiments with benchmark circuits, it is confirmed that our clock gating method is very effective in reducing power, which otherwise the toggling based clock gating shall miss the power saving opportunity, while meeting all timing constraints. Secondly, for reducing the static power consumption, we solve two critical limitations of the conventional approaches to the allocation of state retention storage for power gated circuits. Those are (1) the long wakeup delay caused by the senseless use of multi-bit retention flip-flops (MBRFFs) and (2) the inability to optimize retention flip-flops for the flip-flops with mux-feedback loop. It should be noted that the conventional approaches have regarded the long wakeup delay as an inevitable consequence of maximizing the reduction of total storage size for state retention while they have treated the flip-flops with mux-feedback loop (called self-loop flip-flop) as nonoptimizable component, but practically, the self-loop flip-flops synthesized from hardware description language (HDL) code are not far from a small amount and thus, can in no way be negligible. More precisely, for solving (1), we show that the use of MBRFFs with up to two bits, consequently, constraining the wakeup delay to no more than two clock cycles, is enough to maintain the high reduction of total retention storage and for solving (2), we devise a 2-phase retention control mechanism for a pair of flip-flops, one of which has self-loop, by which just a single retention bit can be used to restore state of the two flip-flops, and propose an independent set based algorithm for maximally extracting the non-conflict pairs from circuits. Through experiments with benchmark circuits, it is shown that our proposed method is very effective against reducing the state retention storage and the power consumption compared with the existing best MBRFF allocation while the wakeup delay is strictly limited to two clock cycles.1 INTRODUCTION 1 1.1 Clock Gating 1 1.2 Power Gating and State Retention 3 1.3 Multi-bit Retention Registers 4 1.4 Contributions of This Dissertation 6 2 FLIP-FLOP STATE DRIVEN CLOCK GATING: CONCEPT, DESIGN, AND METHODOLOGY 9 2.1 Motivations 9 2.1.1 Toggling based Clock Gating 9 2.1.2 Area and Power by Clock Gating 10 2.2 The Proposed Clock Gating 13 2.2.1 Concept of Flip-flop State Driven Clock Gating 13 2.2.2 Design of Gating Logic Circuitry 17 2.2.3 Integrated Clock Gating Methodology 22 2.2.4 Cost Formulation 23 2.3 Experiments 25 2.3.1 Experimental Setup 25 2.3.2 Experimental Results 26 3 ALGORITHM AND DESIGN OPTIMIZATION OF ALLOCATING MULTI-BIT RETENTION FLIP-FLOPS FOR POWER GATED CIRCUITS 32 3.1 Motivations 32 3.1.1 Flip-flops with Mux-feedback Loop 32 3.1.2 Impact of Wakeup Delay 37 3.2 The Proposed Allocation Algorithm 39 3.3 Design of Multi-Bit Retention Flip-Flop and Multi-Bit Extension 48 3.3.1 Multi-Bit Retention Flip-Flop 48 3.3.2 Multi-Bit Flip-Flop Extension 52 3.4 Experiments 54 3.4.1 Experimental Setup 54 3.4.2 Experimental Results 57 4 CONCLUSIONS 65 4.1 Flip-flop State Driven Clock Gating: Concept, Design, and Methodology 65 4.2 Algorithm and Design Optimization of Allocating Multi-bit Retention Flip-flops for Power Gated Circuits 66 Abstract (In Korean) 71Docto

    Benchmark methodologies for the optimized physical synthesis of RISC-V microprocessors

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    As technology continues to advance and chip sizes shrink, the complexity and design time required for integrated circuits have significantly increased. To address these challenges, Electronic Design Automation (EDA) tools have been introduced to streamline the design flow. These tools offer various methodologies and options to optimize power, performance, and chip area. However, selecting the most suitable methods from these options can be challenging, as they may lead to trade-offs among power, performance, and area. While architectural and Register Transfer Level (RTL) optimizations have been extensively studied in existing literature, the impact of optimization methods available in EDA tools on performance has not been thoroughly researched. This thesis aims to optimize a semiconductor processor through EDA tools within the physical synthesis domain to achieve increased performance while maintaining a balance between power efficiency and area utilization. By leveraging floorplanning tools and carefully selecting technology libraries and optimization options, the CV32E40P open-source processor is subjected to various floorplans to analyze their impact on chip performance. The employed techniques, including multibit components prefer option, multiplexer tree prefer option, identification and exclusion of problematic cells, and placement blockages, lead to significant improvements in cell density, congestion mitigation, and timing. The optimized synthesis results demonstrate a 71\% enhancement in chip design performance without a substantial increase in area, showcasing the effectiveness of these techniques in improving large-scale integrated circuits' performance, efficiency, and manufacturability. By exploring and implementing the available options in EDA tools, this study demonstrates how the processor's performance can be significantly improved while maintaining a balanced and efficient chip design. The findings contribute valuable insights to the field of electronic design automation, offering guidance to designers in selecting suitable methodologies for optimizing processors and other integrated circuits

    Fully Automated Radiation Hardened by Design Circuit Construction

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    abstract: A fully automated logic design methodology for radiation hardened by design (RHBD) high speed logic using fine grained triple modular redundancy (TMR) is presented. The hardening techniques used in the cell library are described and evaluated, with a focus on both layout techniques that mitigate total ionizing dose (TID) and latchup issues and flip-flop designs that mitigate single event transient (SET) and single event upset (SEU) issues. The base TMR self-correcting master-slave flip-flop is described and compared to more traditional hardening techniques. Additional refinements are presented, including testability features that disable the self-correction to allow detection of manufacturing defects. The circuit approach is validated for hardness using both heavy ion and proton broad beam testing. For synthesis and auto place and route, the methodology and circuits leverage commercial logic design automation tools. These tools are glued together with custom CAD tools designed to enable easy conversion of standard single redundant hardware description language (HDL) files into hardened TMR circuitry. The flow allows hardening of any synthesizable logic at clock frequencies comparable to unhardened designs and supports standard low-power techniques, e.g. clock gating and supply voltage scaling.Dissertation/ThesisPh.D. Electrical Engineering 201

    Radiation Hardened by Design Methodologies for Soft-Error Mitigated Digital Architectures

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    abstract: Digital architectures for data encryption, processing, clock synthesis, data transfer, etc. are susceptible to radiation induced soft errors due to charge collection in complementary metal oxide semiconductor (CMOS) integrated circuits (ICs). Radiation hardening by design (RHBD) techniques such as double modular redundancy (DMR) and triple modular redundancy (TMR) are used for error detection and correction respectively in such architectures. Multiple node charge collection (MNCC) causes domain crossing errors (DCE) which can render the redundancy ineffectual. This dissertation describes techniques to ensure DCE mitigation with statistical confidence for various designs. Both sequential and combinatorial logic are separated using these custom and computer aided design (CAD) methodologies. Radiation vulnerability and design overhead are studied on VLSI sub-systems including an advanced encryption standard (AES) which is DCE mitigated using module level coarse separation on a 90-nm process with 99.999% DCE mitigation. A radiation hardened microprocessor (HERMES2) is implemented in both 90-nm and 55-nm technologies with an interleaved separation methodology with 99.99% DCE mitigation while achieving 4.9% increased cell density, 28.5 % reduced routing and 5.6% reduced power dissipation over the module fences implementation. A DMR register-file (RF) is implemented in 55 nm process and used in the HERMES2 microprocessor. The RF array custom design and the decoders APR designed are explored with a focus on design cycle time. Quality of results (QOR) is studied from power, performance, area and reliability (PPAR) perspective to ascertain the improvement over other design techniques. A radiation hardened all-digital multiplying pulsed digital delay line (DDL) is designed for double data rate (DDR2/3) applications for data eye centering during high speed off-chip data transfer. The effect of noise, radiation particle strikes and statistical variation on the designed DDL are studied in detail. The design achieves the best in class 22.4 ps peak-to-peak jitter, 100-850 MHz range at 14 pJ/cycle energy consumption. Vulnerability of the non-hardened design is characterized and portions of the redundant DDL are separated in custom and auto-place and route (APR). Thus, a range of designs for mission critical applications are implemented using methodologies proposed in this work and their potential PPAR benefits explored in detail.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201
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