Winter Semester 2010 ”Politehnica” University of Timisoara Course No. 5: Expanding Bio-Inspiration: Towards Reliable MuxTree  Memory Arrays – Part 2 – – Part 2 – Emerging Systems

Presentation Outline Chapter 1: Bio-Inspired Reliability (With a plea for bio- inspiration and a comparison between artificial Embryonics cells and the stem cells from biology) Chapter 2: A Bird’s Eye View Over Faults (Includes fault tolerance motivation, causes of unexpected, soft errors and a description of the physical phenomena involved) Chapter 3: Embryonics and SEUs (Particularities of the project, datapath model in memory structures, and reliability analysis)

Current state-of-the-art Bio-inspired memory for Embryonics genome storage Genome storage critical: –drives actual hardware (polymerase and ribosomic genome) –contains instructions on how additional hardware will be driven (operative genome) No memory protection mechanisms currently Both desirable and feasible Chapter 3: Embryonics and SEUs (1)

3.1. Error-Type Distribution “By far the most common type of chip failure is a soft error of a single cell on a chip” Multiple bit flips 1÷7% of total soft fails recorded Double bit-flips under 5% of the total events 2 cases of quadruple bit flip events witnessed; predicted rate 1 in 65 years per device Chapter 3: Embryonics and SEUs (1)

3.2. Datapath Model for Memory Structures 3D matrix; M rows and N columns of physically identical storage molecules, of F 1- bit memory cells each Data synchronously circled Chapter 3: Embryonics and SEUs (2)

3.2. Datapath Model for Memory Structures For each L i,j a vicinity V(L i,j ) = L x,y L i,j L z,w defined Data shifting process: Chapter 3: Embryonics and SEUs (3)

3.2. Datapath Model for Memory Structures Useful for error injection testing Chapter 3: Embryonics and SEUs (4)

3.3. Reliability Analysis Basic assumption: failures exponentially distributed inside a molecule Similar assumptions found to work well Chapter 3: Embryonics and SEUs (5)

3.4. Error Coding Failure situations: A.Single failure; recovery by parity-based coding B.Double failure; core affected by at least one error, at most two errors on the same row; recovery by Hamming-like codes C.Multiple failure; same as previous, likelihood found to be minimal D.Terminal failure; too many faults, cannot be recovered E.No failures detected; either normal operating or undetectable combination of errors; does not require/ cannot be established recovery measures Chapter 3: Embryonics and SEUs (6)

3.4. Error Coding Strategies of tolerating faults in Embryonics –Fault tolerance at the molecular level Advantage: isolating faulty molecules possible, use of the transparent reconfiguration process; Disadvantage: considerable portion of molecular core affected for redundant coding –Fault tolerance at the macro-cell level Advantage: separate macro-cells for redundant coding and additional logic Disadvantage: reconfiguration process quite difficult due to lack of addressing Chapter 3: Embryonics and SEUs (7)

Macro-Cell Level, Classic SEC Chapter 3: Embryonics and SEUs (8)

Macro-Cell Level, Classic SEC Chapter 3: Embryonics and SEUs (9)

Macro-Cell Level, Protochip SEC Faults in a row superimposed onto a protochip In each protochip, independent Poisson processes formed by failure types a the probability for a type A failure Chapter 3: Embryonics and SEUs (10)

Macro-Cell Level, Protochip SEC Chapter 3: Embryonics and SEUs (11)

Macro-Cell Level, Protochip DEC a the probability for a type A failure Chapter 3: Embryonics and SEUs (12)

Macro-Cell Level, Protochip DEC Chapter 3: Embryonics and SEUs (13)

3.6. Molecular Level Chapter 3: Embryonics and SEUs (14) Molecular reliability λ known

3.6. Molecular Level Chapter 3: Embryonics and SEUs (15) Reliability>90%: 28.4 million hours (SEC) VS 63.3 million hours (DEC) periods; Reliability=50% reached after 89.8 million hours (SEC) VS million hours (DEC)

3.7. Conclusions Chapter 3: Embryonics and SEUs (16) Final expressions of R and MTTF quite complicated Failure rate λ essentially empirical –determined through extensive measurements –may be affected by aggressive environments –constant → variable

3.7. Conclusions Chapter 3: Embryonics and SEUs (17) Unfortunately, no accurate model for cosmic rays Understanding causes and modeling soft fails hot field of research Stochastic nature of soft fails

3.7. Conclusions Chapter 3: Embryonics and SEUs (18) Different macro-cell configurations; may prove too small for real applications Classic reliability analysis difficult, based on non-stochastic parameters Protochip-based analysis with similar results, better suited to other influences (such as cosmic rays)