A sub-threshold cell library and methodology

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 97-102).Sub-threshold operation is a compelling approach for energy-constrained applications where speed is of secondary concern, but increased sensitivity to process variation must be mitigated in this regime. With scaling of process technologies, random within-die variation has recently introduced another degree of complexity in circuit design. This thesis proposes approaches to mitigate process variation in sub-threshold circuits through device sizing, topology selection and fault-tolerant architecture. This thesis makes several contributions to a sub-threshold circuit design methodology. A formal analysis of device sizing trade-offs between delay, energy, and variability reveals that while minimum size devices provide lowest energy and delay in sub-threshold, their increased sensitivity to random dopant fluctuation may cause functional errors. A proposed variation-driven design approach enables consistent sizing of logic gates and registers for constant functional yield. A yield constraint imposes energy overhead at low power supply voltages and changes the minimum energy operating point of a circuit.(cont.) The optimal supply and device sizing depend on the topology of the circuit and its energy versus VDD characteristic. The analysis resulted in a 56-cell library in 65nm CMOS, which is incorporated in a computer-aided design flow. A test chip synthesized from this library implements a fault-tolerant FIR filter. Algorithmic error detection enables correction of transient timing errors due to delay variability in sub-threshold, and also allows the system frequency to be set more aggressively for the average case instead of the worst case.by Joyce Y.S. Kwong.S.M

Low-voltage embedded biomedical processor design

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 180-190).Advances in mobile electronics are fueling new possibilities in a variety of applications, one of which is ambulatory medical monitoring with body-worn or implanted sensors. Digital processors on such sensors serve to analyze signals in real-time and extract key features for transmission or storage. To support diverse and evolving applications, the processor should be flexible, and to extend sensor operating lifetime, the processor should be energy-efficient. This thesis focuses on architectures and circuits for low power biomedical signal processing. A general-purpose processor is extended with custom hardware accelerators to reduce the cycle count and energy for common tasks, including FIR and median filtering as well as computing FFTs and mathematical functions. Improvements to classic architectures are proposed to reduce power and improve versatility: an FFT accelerator demonstrates a new control scheme to reduce datapath switching activity, and a modified CORDIC engine features increased input range and decreased quantization error over conventional designs. At the system level, the addition of accelerators increases leakage power and bus loading; strategies to mitigate these costs are analyzed in this thesis. A key strategy for improving energy efficiency is to aggressively scale the power supply voltage according to application performance demands. However, increased sensitivity to variation at low voltages must be mitigated in logic and SRAM design. For logic circuits, a design flow and a hold time verification methodology addressing local variation are proposed and demonstrated in a 65nm microcontroller functioning at 0.3V. For SRAMs, a model for the weak-cell read current is presented for near-V supply voltages, and a self-timed scheme for reducing internal bus glitches is employed with low leakage overhead. The above techniques are demonstrated in a 0.5-1.OV biomedical signal processing platform in 0.13p-Lm CMOS. The use of accelerators for key signal processing enabled greater than 10x energy reduction in two complete EEG and EKG analysis applications, as compared to implementations on a conventional processor.by Joyce Y. S. Kwong.Ph.D