Improving the Reliability of Microprocessors under BTI and TDDB Degradations

Reliability is a fundamental challenge for current and future microprocessors with advanced nanoscale technologies. With smaller gates, thinner dielectric and higher temperature microprocessors are vulnerable under aging mechanisms such as Bias Temperature Instability (BTI) and Temperature Dependent Dielectric Breakdown (TDDB). Under continuous stress both parametric and functional errors occur, resulting compromised microprocessor lifetime. In this thesis, based on the thorough study on BTI and TDDB mechanisms, solutions are proposed to mitigating the aging processes on memory based and random logic structures in modern out-of-order microprocessors. A large area of processor core is occupied by memory based structure that is vulnerable to BTI induced errors. The problem is exacerbated when PBTI degradation in NMOS is as severe as NBTI in PMOS in high-k metal gate technology. Hence a novel design is proposed to recover 4 internal gates within a SRAM cell simultaneously to mitigate both NBTI and PBTI effects. This technique is applied to both the L2 cache banks and the busy function units with storage cells in out-of-order pipeline in two different ways. For the L2 cache banks, redundant cache bank is added exclusively for proactive recovery rotation. For the critical and busy function units in out-of-order pipelines, idle cycles are exploited at per-buffer-entry level. Different from memory based structures, combinational logic structures such as function units in execution stage can not use low overhead redundancy to tolerate errors due to their irregular structure. A design framework that aims to improve the reliability of the vulnerable functional units of a processor core is designed and implemented. The approach is designing a generic function unit (GFU) that can be reconfigured to replace a particular functional unit (FU) while it is being recovered for improved lifetime. Although flexible, the GFU is slower than the original target FUs. So GFU is carefully designed so as to minimize the performance loss when it is in-use. More schemes are also designed to avoid using the GFU on performance critical paths of a program execution

Metoda projektovanja namenskih programabilnih hardverskih akceleratora

Namenski računarski sistemi se najčesće projektuju tako da mogu da podrže izvršavanje većeg broja željenih aplikacija. Za postizanje što veće efikasnosti, preporučuje se korišćenje specijalizovanih procesora Application Specific Instruction Set Processors–ASIPs, na kojima se izvršavanje programskih instrukcija obavlja u za to projektovanim i nezavisnimhardverskim blokovima (akceleratorima). Glavni razlog za postojanje nezavisnih akceleratora jeste postizanjemaksimalnog ubrzanja izvršavanja instrukcija. Me ¯ dutim, ovakav pristup podrazumeva da je za svaki od blokova potrebno projektovati integrisano (ASIC) kolo, čime se bitno povećava ukupna površina procesora. Metod za smanjenje ukupne površine jeste primena DatapathMerging tehnike na dijagrame toka podataka ulaznih aplikacija. Kao rezultat, dobija se jedan programabilni hardverski akcelerator, sa mogućnosću izvršavanja svih željenih instrukcija. Međutim, ovo ima negativne posledice na efikasnost sistema. često se zanemaruje činjenica da, usled veoma ograničene fleksibilnosti ASIC hardverskih akceleratora, specijalizovani procesori imaju i drugih nedostataka. Naime, u slučaju izmena, ili prosto nadogradnje, specifikacije procesora u završnimfazama projektovanja, neizbežna su velika kašnjenja i dodatni troškovi promene dizajna. U ovoj tezi je pokazano da zahtevi za fleksibilnošću i efikasnošću ne moraju biti međusobno isključivi. Demonstrirano je je da je moguce uneti ograničeni nivo fleksibilnosti hardvera tokom dizajn procesa, tako da dobijeni hardverski akcelerator može da izvršava ne samo aplikacije definisane na samom početku projektovanja, već i druge aplikacije, pod uslovom da one pripadaju istom domenu. Drugim rečima, u tezi je prezentovana metoda projektovanja fleksibilnih namenskih hardverskih akceleratora. Eksperimentalnom evaluacijom pokazano je da su tako dobijeni akceleratori u većini slučajeva samo do 2 x veće površine ili 2 x većeg kašnjenja od akceleratora dobijenih primenom DatapathMerging metode, koja pritom ne pruža ni malo dodatne fleksibilnosti.Typically, embedded systems are designed to support a limited set of target applications. To efficiently execute those applications, they may employ Application Specific Instruction Set Processors (ASIPs) enriched with carefully designed Instructions Set Extension (ISEs) implemented in dedicated hardware blocks. The primary goal when designing ISEs is efficiency, i.e. the highest possible speedup, which implies synthesizing all critical computational kernels of the application dataflow graphs as an Application Specific Integrated Circuit (ASICs). Yet, this can lead to high on-chip area dedicated solely to ISEs. One existing approach to decrease this area by paying a reasonable price of decreased efficiency is to perform datapath merging on input dataflow graphs (DFGs) prior to generating the ASIC. It is often neglected that even higher costs can be accidentally incurred due to the lack of flexibility of such ISEs. Namely, if late design changes or specification upgrades happen, significant time-to-market delays and nonrecurrent costs for redesigning the ISEs and the corresponding ASIPs become inevitable. This thesis shows that flexibility and efficiency are not mutually exclusive. It demonstrates that it is possible to introduce a limited amount of hardware flexibility during the design process, such that the resulting datapath is in fact reconfigurable and thus can execute not only the applications known at design time, but also other applications belonging to the same application-domain. In other words, it proposes a methodology for designing domain-specific reconfigurable arrays out of a limited set of input applications. The experimental results show that resulting arrays are usually around 2£ larger and 2£ slower than ISEs synthesized using datapath merging, which have practically null flexibility beyond the design set of DFGs

Reconciling Specialization and Flexibility Through Compound Circuits.

International audienc

Reconciling Specialization and Flexibility Through Compound Circuits.

International audienc

Reconciling Specialization and Flexibility Through Compound Circuits," presented at

Abstract While parallelism and multi-cores are receiving much attention as a major scalability path, customization is another, orthogonal and complementary, scalability path which can target not easily parallelizable programs or program sections. The key assets of customization are cost and power efficiency. The key limitation of customization is flexibility. However, we argue that there is no perfect balance between efficiency and flexibility, each system vendor may want to strike a different such balance. In this article, we present a method for achieving any desired balance between flexibility and efficiency by automatically combining any set of individual customization circuits into a larger compound circuit. This circuit is significantly more cost efficient than the simple union of all target circuits, and is configurable to behave as any of the target circuits, while avoiding the routing and configuration cost overhead of FPGAs. The more individual circuits are included, the larger the number of applications which can potentially benefit from this compound customization circuit, realizing flexibility at a minimal cost. Moreover, we observe that the compound circuit cost does not increase in proportion to the number of target applications, due to the wide range of common data-flow and control-flow patterns in programs. Currently, the target individual circuits correspond to loops, like most accelerators in embedded systems, but the aggregation method can accommodate circuits of any size. Using the UTDSP benchmarks and accelerators coupled with an embedded PowerPC405 processor, we show that this approach can yield an average performance improvement of 2.97, while the corresponding synthesized aggregate accelerator is 3 time smaller than the sum of individual accelerators for each target benchmark