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Periferie procesoru RISC-V
Vavro, Tomáš ; Kekely, Lukáš (oponent) ; Martínek, Tomáš (vedoucí práce)
Platforma RISC-V je jedným z lídrov v odvetví počítačových a vstavaných systémov. Pri čoraz väčšej miere využívania takýchto systémov rastie dopyt po dostupných perifériách pre implementácie tejto platformy. Táto práca sa zaoberá procesorom FU540-C000 od spoločnosti SiFive, ktorý je jednou z implementácií architektúry RISC-V, a jeho základnými perifériami. Na základe analýzy bol spomedzi periférií tohoto procesoru zvolený obvod UART slúžiaci pre asynchrónnu sériovú komunikáciu. Cieľom tejto diplomovej práce je danú perifériu navrhnúť a implementovať v niektorom z jazykov pre popis číslicových obvodov, a následne vytvoriť verifikačné prostredie, prostredníctvom ktorého bude overená funkčnosť implementácie.
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New Methods for Increasing Efficiency and Speed of Functional Verification
Zachariášová, Marcela ; Dohnal, Jan (oponent) ; Steininger, Andreas (oponent) ; Kotásek, Zdeněk (vedoucí práce)
In the development of current hardware systems, e.g. embedded systems or computer hardware, new ways how to increase their reliability are highly investigated. One way how to tackle the issue of reliability is to increase the efficiency and the speed of verification processes that are performed in the early phases of the design cycle. In this Ph.D. thesis, the attention is focused on the verification approach called functional verification. Several challenges and problems connected with the efficiency and the speed of functional verification are identified and reflected in the goals of the Ph.D. thesis. The first goal focuses on the reduction of the simulation runtime when verifying complex hardware systems. The reason is that the simulation of inherently parallel hardware systems is very slow in comparison to the speed of real hardware. The optimization technique is proposed that moves the verified system into the FPGA acceleration board while the rest of the verification environment runs in simulation. By this single move, the simulation overhead can be significantly reduced. The second goal deals with manually written verification environments which represent a huge bottleneck in the verification productivity. However, it is not reasonable, because almost all verification environments have the same structure as they utilize libraries of basic components from the standard verification methodologies. They are only adjusted to the system that is verified. Therefore, the second optimization technique takes the high-level specification of the system and then automatically generates a comprehensive verification environment for this system. The third goal elaborates how the completeness of the verification process can be achieved using the intelligent automation. The completeness is measured by different coverage metrics and the verification is usually ended when a satisfying level of coverage is achieved. Therefore, the third optimization technique drives generation of input stimuli in order to activate multiple coverage points in the veri\-fied system and to enhance the overall coverage rate. As the main optimization tool the genetic algorithm is used, which is adopted for the functional verification purposes and its parameters are well-tuned for this domain. It is running in the background of the verification process, it analyses the coverage and it dynamically changes constraints of the stimuli generator. Constraints are represented by the probabilities using which particular values from the input domain are selected. The fourth goal discusses the re-usability of verification stimuli for regression testing and how these stimuli can be further optimized in order to speed-up the testing. It is quite common in verification that until a satisfying level of coverage is achieved, many redundant stimuli are evaluated as they are produced by pseudo-random generators. However, when creating optimal regression suites, redundancy is not needed anymore and can be removed. At the same time, it is important to retain the same level of coverage in order to check all the key properties of the system. The fourth optimization technique is also based on the genetic algorithm, but it is not integrated into the verification process but works offline after the verification is ended. It removes the redundancy from the original suite of stimuli very fast and effectively so the resulting verification runtime of the regression suite is significantly improved.
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Periferie procesoru RISC-V
Vavro, Tomáš ; Kekely, Lukáš (oponent) ; Martínek, Tomáš (vedoucí práce)
Platforma RISC-V je jedným z lídrov v odvetví počítačových a vstavaných systémov. Pri čoraz väčšej miere využívania takýchto systémov rastie dopyt po dostupných perifériách pre implementácie tejto platformy. Táto práca sa zaoberá procesorom FU540-C000 od spoločnosti SiFive, ktorý je jednou z implementácií architektúry RISC-V, a jeho základnými perifériami. Na základe analýzy bol spomedzi periférií tohoto procesoru zvolený obvod UART slúžiaci pre asynchrónnu sériovú komunikáciu. Cieľom tejto diplomovej práce je danú perifériu navrhnúť a implementovať v niektorom z jazykov pre popis číslicových obvodov, a následne vytvoriť verifikačné prostredie, prostredníctvom ktorého bude overená funkčnosť implementácie.
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New Methods for Increasing Efficiency and Speed of Functional Verification
Zachariášová, Marcela ; Dohnal, Jan (oponent) ; Steininger, Andreas (oponent) ; Kotásek, Zdeněk (vedoucí práce)
In the development of current hardware systems, e.g. embedded systems or computer hardware, new ways how to increase their reliability are highly investigated. One way how to tackle the issue of reliability is to increase the efficiency and the speed of verification processes that are performed in the early phases of the design cycle. In this Ph.D. thesis, the attention is focused on the verification approach called functional verification. Several challenges and problems connected with the efficiency and the speed of functional verification are identified and reflected in the goals of the Ph.D. thesis. The first goal focuses on the reduction of the simulation runtime when verifying complex hardware systems. The reason is that the simulation of inherently parallel hardware systems is very slow in comparison to the speed of real hardware. The optimization technique is proposed that moves the verified system into the FPGA acceleration board while the rest of the verification environment runs in simulation. By this single move, the simulation overhead can be significantly reduced. The second goal deals with manually written verification environments which represent a huge bottleneck in the verification productivity. However, it is not reasonable, because almost all verification environments have the same structure as they utilize libraries of basic components from the standard verification methodologies. They are only adjusted to the system that is verified. Therefore, the second optimization technique takes the high-level specification of the system and then automatically generates a comprehensive verification environment for this system. The third goal elaborates how the completeness of the verification process can be achieved using the intelligent automation. The completeness is measured by different coverage metrics and the verification is usually ended when a satisfying level of coverage is achieved. Therefore, the third optimization technique drives generation of input stimuli in order to activate multiple coverage points in the veri\-fied system and to enhance the overall coverage rate. As the main optimization tool the genetic algorithm is used, which is adopted for the functional verification purposes and its parameters are well-tuned for this domain. It is running in the background of the verification process, it analyses the coverage and it dynamically changes constraints of the stimuli generator. Constraints are represented by the probabilities using which particular values from the input domain are selected. The fourth goal discusses the re-usability of verification stimuli for regression testing and how these stimuli can be further optimized in order to speed-up the testing. It is quite common in verification that until a satisfying level of coverage is achieved, many redundant stimuli are evaluated as they are produced by pseudo-random generators. However, when creating optimal regression suites, redundancy is not needed anymore and can be removed. At the same time, it is important to retain the same level of coverage in order to check all the key properties of the system. The fourth optimization technique is also based on the genetic algorithm, but it is not integrated into the verification process but works offline after the verification is ended. It removes the redundancy from the original suite of stimuli very fast and effectively so the resulting verification runtime of the regression suite is significantly improved.
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