5 research outputs found
Towards Logic Functions as the Device using Spin Wave Functions Nanofabric
As CMOS technology scaling is fast approaching its fundamental limits, several new nano-electronic devices have been proposed as possible alternatives to MOSFETs. Research on emerging devices mainly focusses on improving the intrinsic characteristics of these single devices keeping the overall integration approach fairly conventional. However, due to high logic complexity and wiring requirements, the overall system-level power, performance and area do not scale proportional to that of individual devices.
Thereby, we propose a fundamental shift in mindset, to make the devices themselves more functional than simple switches. Our goal in this thesis is to develop a new nanoscale fabric paradigm that enables realization of arbitrary logic functions (with high fan-in/fan-out) more efficiently. We leverage on non-equilibrium spin wave physical phenomenon and wave interference to realize these elementary functions called Spin Wave Functions (SPWFs).
In the proposed fabric, computation is based on the principle of wave superposition. Information is encoded both in the phase and amplitude of spin waves; thereby providing an opportunity for compressed data representation. Moreover, spin wave propagation does not involve any physical movement of charge particles. This provides a fundamental advantage over conventional charge based electronics and opens new horizons for novel nano-scale architectures.
We show several variants of the SPWFs based on topology, signal weights, control inputs and wave frequencies. SPWF based designs of arithmetic circuits like adders and parallel counters are presented. Our efforts towards developing new architectures using SPWFs places strong emphasis on integrated fabric-circuit exploration methodology. With different topologies and circuit styles we have explored how capabilities at individual fabric components level can affect design and vice versa. Our estimates on benefits vs. 45nm CMOS implementation show that, for a 1-bit adder, up to 40x reduction in area and 228x reduction in power is possible. For the 2-bit adder, results show that up to 33x area reduction and 222x reduction in power may be possible.
Building large scale SPWF-based systems, requires mechanisms for synchronization and data streaming. In this thesis, we present data streaming approaches based on Asynchronous SPWFs (A-SPWFs). As an example, a 32-bit Carry Completion Sensing Adder (CCSA) is shown based on the A-SPWF approach with preliminary power, performance and area evaluations
Recommended from our members
Parameter Variation Sensing and Estimation in Nanoscale Fabrics
Parameter variations introduced by manufacturing imprecision are becoming more influential on circuit performance. This is especially the case in emerging nanoscale fabrics due to unconventional manufacturing steps (e.g., nano-imprint) and aggressive scaling. These parameter variations can lead to performance deterioration and consequently yield loss.
Parameter variations are typically addressed pre-fabrication with circuit design targeting worst-case timing scenarios. However, this approach is pessimistic and much of performance benefits can be lost. By contrast, if parameter variations can be estimated post-manufacturing, adaptive techniques or reconfiguration could be used to provide more optimal level of tolerance. To estimate parameter variations during run-time, on-chip variation sensors are gaining in importance because of their easy implementation.
In this thesis, we propose novel on-chip variation sensors to estimate variations in physical parameters for emerging nanoscale fabrics. Based on the characteristics of systematic and random variations, two separate sensors are designed to estimate the extent of systematic variations and the statistical distribution of random variations from measured fall and rise times in the sensors respectively. The proposed sensor designs are evaluated through HSPICE Monte Carlo simulations with known variation cases injected. Simulation results show that the estimation error of the systematic-variation sensor is less than 1.2% for all simulated cases; and for the random-variation sensor, the worst-case estimation error is 12.7% and the average estimation error is 8% for all simulations.
In addition, to address the placement of on-chip sensors, we calculate sensor area and the effective range of systematic-variation sensor. Then using a processor designed in nanoscale fabrics as a target, an example for sensor placement is introduced. Based on the sensor placement, external noises that may affect the measured fall and rise times of outputs are identified. Through careful analysis, we find that these noises do not deteriorate the accuracy of the systematic-variation sensor, but affect the accuracy of the random-variation sensor.
We believe that the proposed on-chip variation sensors in conjunction with post-fabrication compensation techniques would be able to improve system-level performance in nanoscale fabrics, which may be an efficient alternative to making worst-case assumptions on parameter variations in nanoscale designs