347 research outputs found

    ์ƒ๋ณ€ํ™” ๋ฉ”๋ชจ๋ฆฌ ์‹œ์Šคํ…œ์˜ ๊ฐ„์„ญ ์˜ค๋ฅ˜ ์™„ํ™” ๋ฐ RMW ์„ฑ๋Šฅ ํ–ฅ์ƒ ๊ธฐ๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2021.8. ์ดํ˜์žฌ.Phase-change memory (PCM) announces the beginning of the new era of memory systems, owing to attractive characteristics. Many memory product manufacturers (e.g., Intel, SK Hynix, and Samsung) are developing related products. PCM can be applied to various circumstances; it is not simply limited to an extra-scale database. For example, PCM has a low standby power due to its non-volatility; hence, computation-intensive applications or mobile applications (i.e., long memory idle time) are suitable to run on PCM-based computing systems. Despite these fascinating features of PCM, PCM is still far from the general commercial market due to low reliability and long latency problems. In particular, low reliability is a painful problem for PCM in past decades. As the semiconductor process technology rapidly scales down over the years, DRAM reaches 10 nm class process technology. In addition, it is reported that the write disturbance error (WDE) would be a serious issue for PCM if it scales down below 54 nm class process technology. Therefore, addressing the problem of WDEs becomes essential to make PCM competitive to DRAM. To overcome this problem, this dissertation proposes a novel approach that can restore meta-stable cells on demand by levering two-level SRAM-based tables, thereby significantly reducing the number WDEs. Furthermore, a novel randomized approach is proposed to implement a replacement policy that originally requires hundreds of read ports on SRAM. The second problem of PCM is a long-latency compared to that of DRAM. In particular, PCM tries to enhance its throughput by adopting a larger transaction unit; however, the different unit size from the general-purpose processor cache line further degrades the system performance due to the introduction of a read-modify-write (RMW) module. Since there has never been any research related to RMW in a PCM-based memory system, this dissertation proposes a novel architecture to enhance the overall system performance and reliability of a PCM-based memory system having an RMW module. The proposed architecture enhances data re-usability without introducing extra storage resources. Furthermore, a novel operation that merges commands regardless of command types is proposed to enhance performance notably. Another problem is the absence of a full simulation platform for PCM. While the announced features of the PCM-related product (i.e., Intel Optane) are scarce due to confidential issues, all priceless information can be integrated to develop an architecture simulator that resembles the available product. To this end, this dissertation tries to scrape up all available features of modules in a PCM controller and implement a dedicated simulator for future research purposes.์ƒ๋ณ€ํ™” ๋ฉ”๋ชจ๋ฆฌ๋Š”(PCM) ๋งค๋ ฅ์ ์ธ ํŠน์„ฑ์„ ํ†ตํ•ด ๋ฉ”๋ชจ๋ฆฌ ์‹œ์Šคํ…œ์˜ ์ƒˆ๋กœ์šด ์‹œ๋Œ€์˜ ์‹œ์ž‘์„ ์•Œ๋ ธ๋‹ค. ๋งŽ์€ ๋ฉ”๋ชจ๋ฆฌ ๊ด€๋ จ ์ œํ’ˆ ์ œ์กฐ์—…์ฒด(์˜ˆ : ์ธํ…”, SK ํ•˜์ด๋‹‰์Šค, ์‚ผ์„ฑ)๊ฐ€ ๊ด€๋ จ ์ œํ’ˆ ๊ฐœ๋ฐœ์— ๋ฐ•์ฐจ๋ฅผ ๊ฐ€ํ•˜๊ณ  ์žˆ๋‹ค. PCM์€ ๋‹จ์ˆœํžˆ ๋Œ€๊ทœ๋ชจ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค์—๋งŒ ๊ตญํ•œ๋˜์ง€ ์•Š๊ณ  ๋‹ค์–‘ํ•œ ์ƒํ™ฉ์— ์ ์šฉ๋  ์ˆ˜ ์žˆ๋‹ค. ์˜ˆ๋ฅผ ๋“ค์–ด, PCM์€ ๋น„ํœ˜๋ฐœ์„ฑ์œผ๋กœ ์ธํ•ด ๋Œ€๊ธฐ ์ „๋ ฅ์ด ๋‚ฎ๋‹ค. ๋”ฐ๋ผ์„œ ๊ณ„์‚ฐ ์ง‘์•ฝ์ ์ธ ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜ ๋˜๋Š” ๋ชจ๋ฐ”์ผ ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜์€(์ฆ‰, ๊ธด ๋ฉ”๋ชจ๋ฆฌ ์œ ํœด ์‹œ๊ฐ„) PCM ๊ธฐ๋ฐ˜ ์ปดํ“จํŒ… ์‹œ์Šคํ…œ์—์„œ ์‹คํ–‰ํ•˜๊ธฐ์— ์ ํ•ฉํ•˜๋‹ค. PCM์˜ ์ด๋Ÿฌํ•œ ๋งค๋ ฅ์ ์ธ ํŠน์„ฑ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  PCM์€ ๋‚ฎ์€ ์‹ ๋ขฐ์„ฑ๊ณผ ๊ธด ๋Œ€๊ธฐ ์‹œ๊ฐ„์œผ๋กœ ์ธํ•ด ์—ฌ์ „ํžˆ ์ผ๋ฐ˜ ์‚ฐ์—… ์‹œ์žฅ์—์„œ๋Š” DRAM๊ณผ ๋‹ค์†Œ ๊ฒฉ์ฐจ๊ฐ€ ์žˆ๋‹ค. ํŠนํžˆ ๋‚ฎ์€ ์‹ ๋ขฐ์„ฑ์€ ์ง€๋‚œ ์ˆ˜์‹ญ ๋…„ ๋™์•ˆ PCM ๊ธฐ์ˆ ์˜ ๋ฐœ์ „์„ ์ €ํ•ดํ•˜๋Š” ๋ฌธ์ œ๋‹ค. ๋ฐ˜๋„์ฒด ๊ณต์ • ๊ธฐ์ˆ ์ด ์ˆ˜๋…„์— ๊ฑธ์ณ ๋น ๋ฅด๊ฒŒ ์ถ•์†Œ๋จ์— ๋”ฐ๋ผ DRAM์€ 10nm ๊ธ‰ ๊ณต์ • ๊ธฐ์ˆ ์— ๋„๋‹ฌํ•˜์˜€๋‹ค. ์ด์–ด์„œ, ์“ฐ๊ธฐ ๋ฐฉํ•ด ์˜ค๋ฅ˜ (WDE)๊ฐ€ 54nm ๋“ฑ๊ธ‰ ํ”„๋กœ์„ธ์Šค ๊ธฐ์ˆ  ์•„๋ž˜๋กœ ์ถ•์†Œ๋˜๋ฉด PCM์— ์‹ฌ๊ฐํ•œ ๋ฌธ์ œ๊ฐ€ ๋  ๊ฒƒ์œผ๋กœ ๋ณด๊ณ ๋˜์—ˆ๋‹ค. ๋”ฐ๋ผ์„œ, WDE ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๋Š” ๊ฒƒ์€ PCM์ด DRAM๊ณผ ๋™๋“ฑํ•œ ๊ฒฝ์Ÿ๋ ฅ์„ ๊ฐ–์ถ”๋„๋ก ํ•˜๋Š” ๋ฐ ์žˆ์–ด ํ•„์ˆ˜์ ์ด๋‹ค. ์ด ๋ฌธ์ œ๋ฅผ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•ด ์ด ๋…ผ๋ฌธ์—์„œ๋Š” 2-๋ ˆ๋ฒจ SRAM ๊ธฐ๋ฐ˜ ํ…Œ์ด๋ธ”์„ ํ™œ์šฉํ•˜์—ฌ WDE ์ˆ˜๋ฅผ ํฌ๊ฒŒ ์ค„์—ฌ ํ•„์š”์— ๋”ฐ๋ผ ์ค€ ์•ˆ์ • ์…€์„ ๋ณต์›ํ•  ์ˆ˜ ์žˆ๋Š” ์ƒˆ๋กœ์šด ์ ‘๊ทผ ๋ฐฉ์‹์„ ์ œ์•ˆํ•œ๋‹ค. ๋˜ํ•œ, ์›๋ž˜ SRAM์—์„œ ์ˆ˜๋ฐฑ ๊ฐœ์˜ ์ฝ๊ธฐ ํฌํŠธ๊ฐ€ ํ•„์š”ํ•œ ๋Œ€์ฒด ์ •์ฑ…์„ ๊ตฌํ˜„ํ•˜๊ธฐ ์œ„ํ•ด ์ƒˆ๋กœ์šด ๋žœ๋ค ๊ธฐ๋ฐ˜์˜ ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. PCM์˜ ๋‘ ๋ฒˆ์งธ ๋ฌธ์ œ๋Š” DRAM์— ๋น„ํ•ด ์ง€์—ฐ ์‹œ๊ฐ„์ด ๊ธธ๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ํŠนํžˆ PCM์€ ๋” ํฐ ํŠธ๋žœ์žญ์…˜ ๋‹จ์œ„๋ฅผ ์ฑ„ํƒํ•˜์—ฌ ๋‹จ์œ„์‹œ๊ฐ„ ๋‹น ๋ฐ์ดํ„ฐ ์ฒ˜๋ฆฌ๋Ÿ‰ ํ–ฅ์ƒ์„ ๋„๋ชจํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ฒ”์šฉ ํ”„๋กœ์„ธ์„œ ์บ์‹œ ๋ผ์ธ๊ณผ ๋‹ค๋ฅธ ์œ ๋‹› ํฌ๊ธฐ๋Š” ์ฝ๊ธฐ-์ˆ˜์ •-์“ฐ๊ธฐ (RMW) ๋ชจ๋“ˆ์˜ ๋„์ž…์œผ๋กœ ์ธํ•ด ์‹œ์Šคํ…œ ์„ฑ๋Šฅ์„ ์ €ํ•˜ํ•˜๊ฒŒ ๋œ๋‹ค. PCM ๊ธฐ๋ฐ˜ ๋ฉ”๋ชจ๋ฆฌ ์‹œ์Šคํ…œ์—์„œ RMW ๊ด€๋ จ ์—ฐ๊ตฌ๊ฐ€ ์—†์—ˆ๊ธฐ ๋•Œ๋ฌธ์— ๋ณธ ๋…ผ๋ฌธ์€ RMW ๋ชจ๋“ˆ์„ ํƒ‘์žฌ ํ•œ PCM ๊ธฐ๋ฐ˜ ๋ฉ”๋ชจ๋ฆฌ ์‹œ์Šคํ…œ์˜ ์ „๋ฐ˜์ ์ธ ์‹œ์Šคํ…œ ์„ฑ๋Šฅ๊ณผ ์‹ ๋ขฐ์„ฑ์„ ํ–ฅ์ƒํ•˜๊ฒŒ ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋Š” ์ƒˆ๋กœ์šด ์•„ํ‚คํ…์ฒ˜๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆ๋œ ์•„ํ‚คํ…์ฒ˜๋Š” ์ถ”๊ฐ€ ์Šคํ† ๋ฆฌ์ง€ ๋ฆฌ์†Œ์Šค๋ฅผ ๋„์ž…ํ•˜์ง€ ์•Š๊ณ ๋„ ๋ฐ์ดํ„ฐ ์žฌ์‚ฌ์šฉ์„ฑ์„ ํ–ฅ์ƒ์‹œํ‚จ๋‹ค. ๋˜ํ•œ, ์„ฑ๋Šฅ ํ–ฅ์ƒ์„ ์œ„ํ•ด ๋ช…๋ น ์œ ํ˜•๊ณผ ๊ด€๊ณ„์—†์ด ๋ช…๋ น์„ ๋ณ‘ํ•ฉํ•˜๋Š” ์ƒˆ๋กœ์šด ์ž‘์—…์„ ์ œ์•ˆํ•œ๋‹ค. ๋˜ ๋‹ค๋ฅธ ๋ฌธ์ œ๋Š” PCM์„ ์œ„ํ•œ ์™„์ „ํ•œ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ํ”Œ๋žซํผ์ด ๋ถ€์žฌํ•˜๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. PCM ๊ด€๋ จ ์ œํ’ˆ(์˜ˆ : Intel Optane)์— ๋Œ€ํ•ด ๋ฐœํ‘œ๋œ ์ •๋ณด๋Š” ๋Œ€์™ธ๋น„ ๋ฌธ์ œ๋กœ ์ธํ•ด ๋ถ€์กฑํ•˜๋‹ค. ํ•˜์ง€๋งŒ ์•Œ๋ ค์ ธ ์žˆ๋Š” ์ •๋ณด๋ฅผ ์ ์ ˆํžˆ ์ทจํ•ฉํ•˜๋ฉด ์‹œ์ค‘ ์ œํ’ˆ๊ณผ ์œ ์‚ฌํ•œ ์•„ํ‚คํ…์ฒ˜ ์‹œ๋ฎฌ๋ ˆ์ดํ„ฐ๋ฅผ ๊ฐœ๋ฐœํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ๋ณธ ๋…ผ๋ฌธ์€ PCM ๋ฉ”๋ชจ๋ฆฌ ์ปจํŠธ๋กค๋Ÿฌ์— ํ•„์š”ํ•œ ๋ชจ๋“  ๋ชจ๋“ˆ ์ •๋ณด๋ฅผ ํ™œ์šฉํ•˜์—ฌ ํ–ฅํ›„ ์ด์™€ ๊ด€๋ จ๋œ ์—ฐ๊ตฌ์—์„œ ์ถฉ๋ถ„ํžˆ ์‚ฌ์šฉ ๊ฐ€๋Šฅํ•œ ์ „์šฉ ์‹œ๋ฎฌ๋ ˆ์ดํ„ฐ๋ฅผ ๊ตฌํ˜„ํ•˜์˜€๋‹ค.1 INTRODUCTION 1 1.1 Limitation of Traditional Main Memory Systems 1 1.2 Phase-Change Memory as Main Memory 3 1.2.1 Opportunities of PCM-based System 3 1.2.2 Challenges of PCM-based System 4 1.3 Dissertation Overview 7 2 BACKGROUND AND PREVIOUS WORK 8 2.1 Phase-Change Memory 8 2.2 Mitigation Schemes for Write Disturbance Errors 10 2.2.1 Write Disturbance Errors 10 2.2.2 Verification and Correction 12 2.2.3 Lazy Correction 13 2.2.4 Data Encoding-based Schemes 14 2.2.5 Sparse-Insertion Write Cache 16 2.3 Performance Enhancement for Read-Modify-Write 17 2.3.1 Traditional Read-Modify-Write 17 2.3.2 Write Coalescing for RMW 19 2.4 Architecture Simulators for PCM 21 2.4.1 NVMain 21 2.4.2 Ramulator 22 2.4.3 DRAMsim3 22 3 IN-MODULE DISTURBANCE BARRIER 24 3.1 Motivation 25 3.2 IMDB: In Module-Disturbance Barrier 29 3.2.1 Architectural Overview 29 3.2.2 Implementation of Data Structures 30 3.2.3 Modification of Media Controller 36 3.3 Replacement Policy 38 3.3.1 Replacement Policy for IMDB 38 3.3.2 Approximate Lowest Number Estimator 40 3.4 Putting All Together: Case Studies 43 3.5 Evaluation 45 3.5.1 Configuration 45 3.5.2 Architectural Exploration 47 3.5.3 Effectiveness of the Replacement Policy 48 3.5.4 Sensitivity to Main Table Configuration 49 3.5.5 Sensitivity to Barrier Buffer Size 51 3.5.6 Sensitivity to AppLE Group Size 52 3.5.7 Comparison with Other Studies 54 3.6 Discussion 59 3.7 Summary 63 4 INTEGRATION OF AN RMW MODULE IN A PCM-BASED SYSTEM 64 4.1 Motivation 65 4.2 Utilization of DRAM Cache for RMW 67 4.2.1 Architectural Design 67 4.2.2 Algorithm 70 4.3 Typeless Command Merging 73 4.3.1 Architectural Design 73 4.3.2 Algorithm 74 4.4 An Alternative Implementation: SRC-RMW 78 4.4.1 Implementation of SRC-RMW 78 4.4.2 Design Constraint 80 4.5 Case Study 82 4.6 Evaluation 85 4.6.1 Configuration 85 4.6.2 Speedup 88 4.6.3 Read Reliability 91 4.6.4 Energy Consumption: Selecting a Proper Page Size 93 4.6.5 Comparison with Other Studies 95 4.7 Discussion 97 4.8 Summary 99 5 AN ALL-INCLUSIVE SIMULATOR FOR A PCM CONTROLLER 100 5.1 Motivation 101 5.2 PCMCsim: PCM Controller Simulator 103 5.2.1 Architectural Overview 103 5.2.2 Underlying Classes of PCMCsim 104 5.2.3 Implementation of Contention Behavior 108 5.2.4 Modules of PCMCsim 109 5.3 Evaluation 116 5.3.1 Correctness of the Simulator 116 5.3.2 Comparison with Other Simulators 117 5.4 Summary 119 6 Conclusion 120 Abstract (In Korean) 141 Acknowledgment 143๋ฐ•

    A Multi-layer Fpga Framework Supporting Autonomous Runtime Partial Reconfiguration

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    Partial reconfiguration is a unique capability provided by several Field Programmable Gate Array (FPGA) vendors recently, which involves altering part of the programmed design within an SRAM-based FPGA at run-time. In this dissertation, a Multilayer Runtime Reconfiguration Architecture (MRRA) is developed, evaluated, and refined for Autonomous Runtime Partial Reconfiguration of FPGA devices. Under the proposed MRRA paradigm, FPGA configurations can be manipulated at runtime using on-chip resources. Operations are partitioned into Logic, Translation, and Reconfiguration layers along with a standardized set of Application Programming Interfaces (APIs). At each level, resource details are encapsulated and managed for efficiency and portability during operation. An MRRA mapping theory is developed to link the general logic function and area allocation information to the device related physical configuration level data by using mathematical data structure and physical constraints. In certain scenarios, configuration bit stream data can be read and modified directly for fast operations, relying on the use of similar logic functions and common interconnection resources for communication. A corresponding logic control flow is also developed to make the entire process autonomous. Several prototype MRRA systems are developed on a Xilinx Virtex II Pro platform. The Virtex II Pro on-chip PowerPC core and block RAM are employed to manage control operations while multiple physical interfaces establish and supplement autonomous reconfiguration capabilities. Area, speed and power optimization techniques are developed based on the developed Xilinx prototype. Evaluations and analysis of these prototype and techniques are performed on a number of benchmark and hashing algorithm case studies. The results indicate that based on a variety of test benches, up to 70% reduction in the resource utilization, up to 50% improvement in power consumption, and up to 10 times increase in run-time performance are achieved using the developed architecture and approaches compared with Xilinx baseline reconfiguration flow. Finally, a Genetic Algorithm (GA) for a FPGA fault tolerance case study is evaluated as a ultimate high-level application running on this architecture. It demonstrated that this is a hardware and software infrastructure that enables an FPGA to dynamically reconfigure itself efficiently under the control of a soft microprocessor core that is instantiated within the FPGA fabric. Such a system contributes to the observed benefits of intelligent control, fast reconfiguration, and low overhead

    Reconfigurable-Hardware Accelerated Stream Aggregation

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    High throughput and low latency stream aggregation is essential for many applications that analyze massive volumes of data in real-time. Incoming data need to be stored in a single sliding-window before processing, in cases where incremental aggregations are wasteful or not possible at all. However, storing all incoming values in a single-window puts tremendous pressure on the memory bandwidth and capacity. GPU and CPU memory management is inefficient for this task as it introduces unnecessary data movement that wastes bandwidth. FPGAs can make more efficient use of their memory but existing approaches employ only on-chip memory and therefore, can only support small problem sizes (i.e. small sliding windows and number of keys) due to the limited capacity. This thesis addresses the above limitations of stream processing systems by proposing techniques for accelerating single sliding-window stream aggregation using FPGAs to achieve line-rate processing throughput and ultra low latency. It does so first by building accelerators using FPGAs and second, by alleviating the memory pressure posed by single-window stream aggregation. The initial part of this thesis presents the accelerators for both windowing policies, namely, tuple- and time-\ua0based, using Maxeler\u27s DataFlow Engines\ua0(DFEs) which have a direct feed of incoming data from the network as well as direct access to off-chip DRAM. Compared to state-of-the-art stream processing software system, the DFEs offer 1-2 orders of magnitude higher processing throughput and 4 orders of magnitude lower latency. The later part of this thesis focuses on alleviating the memory pressure due to the various steps in single-window stream aggregation. Updating the window with new incoming values and reading it to feed the aggregation functions are the two primary steps in stream aggregation. The high on-chip SRAM bandwidth enables line-rate processing, but only for small problem sizes due to the limited capacity. The larger off-chip DRAM size supports larger problems, but falls short on performance due to lower bandwidth. In order to bridge this gap, this thesis introduces a specialized memory hierarchy for stream aggregation. It employs Multi-Level Queues (MLQs) spanning across multiple memory levels with different characteristics to offer both high bandwidth and capacity. In doing so, larger stream aggregation problems can be supported at line-rate performance, outperforming existing competing solutions. Compared to designs with only on-chip memory, our approach supports 4 orders of magnitude larger problems. Compared to designs that use only DRAM, our design achieves up to 8x higher throughput. Finally, this thesis aims to alleviate the memory pressure due to the window-aggregation step. Although window-updates can be supported efficiently using MLQs, frequent window-aggregations remain a performance bottleneck. This thesis addresses this problem by introducing StreamZip, a dataflow stream aggregation engine that is able to compress the sliding-windows. StreamZip deals with a number of data and control dependency challenges to integrate a compressor in the stream aggregation pipeline and alleviate the memory pressure posed by frequent aggregations. In doing so, StreamZip offers higher throughput as well as larger effective window capacity to support larger problems. StreamZip supports diverse compression algorithms offering both lossless and lossy compression to fixed- as well as floating- point numbers. Compared to designs using MLQs, StreamZip lossless and lossy designs achieve up to 7.5x and 22x higher throughput, while improving the effective memory capacity by up to 5x and 23x, respectively

    Design of a low-cost high speed data capture card for the Hubble Sphere Hydrogen Survey

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    Includes bibliographical references (leaves 101-105).This thesis describes the design and implementation of a low-cost high speed data capture card for the Hubble Sphere Hydrogen Survey (HSHS). The Hubble Space Hydrogen Survey was initiated in an effort to build a low-cost cylindrical radio telescope for an all sky redshift survey with the observational goal to produce a 3-dimensional mapping of the bulk Hubble Sphere using Hydrogen 21cm emissions. This dissertation รฏยฌ rst investigates the system design to see how each of the user speciรฏยฌ cations set by the planning team could be achieved in terms of design decisions, component selection and schematic capture. The final design. AstroGIG, satisรฏยฌ es the user speciรฏยฌ cations by capturing data up to a full power bandwidth of 1.7GHz with an instantaneous bandwidth of รขโ€ฐยค 250MHz white maximizing the dynamic range. AstroGIG buffers, processes, stores and รฏยฌ nally transmits the data through a 4-lane PCI-Express interface to a standard PC where the majority of the processing is performed. The system implementation is then described where issues relating to the process of transforming schematics into a physical PCB, and HSHS integration are discussed. The design is veriรฏยฌ ed through Hyperlynx simulations to give a high degree of certainty that physical implementation and production would be successful. Results from tests on the actual hardware characterizing the overall system performance are presented. Conclusions are drawn based on these results and suggestions for future work and design improvements are recommended
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