MFPA: Mixed-Signal Field Programmable Array for Energy-Aware Compressive Signal Processing

Compressive Sensing (CS) is a signal processing technique which reduces the number of samples taken per frame to decrease energy, storage, and data transmission overheads, as well as reducing time taken for data acquisition in time-critical applications. The tradeoff in such an approach is increased complexity of signal reconstruction. While several algorithms have been developed for CS signal reconstruction, hardware implementation of these algorithms is still an area of active research. Prior work has sought to utilize parallelism available in reconstruction algorithms to minimize hardware overheads; however, such approaches are limited by the underlying limitations in CMOS technology. Herein, the MFPA (Mixed-signal Field Programmable Array) approach is presented as a hybrid spin-CMOS reconfigurable fabric specifically designed for implementation of CS data sampling and signal reconstruction. The resulting fabric consists of 1) slice-organized analog blocks providing amplifiers, transistors, capacitors, and Magnetic Tunnel Junctions (MTJs) which are configurable to achieving square/square root operations required for calculating vector norms, 2) digital functional blocks which feature 6-input clockless lookup tables for computation of matrix inverse, and 3) an MRAM-based nonvolatile crossbar array for carrying out low-energy matrix-vector multiplication operations. The various functional blocks are connected via a global interconnect and spin-based analog-to-digital converters. Simulation results demonstrate significant energy and area benefits compared to equivalent CMOS digital implementations for each of the functional blocks used: this includes an 80% reduction in energy and 97% reduction in transistor count for the nonvolatile crossbar array, 80% standby power reduction and 25% reduced area footprint for the clockless lookup tables, and roughly 97% reduction in transistor count for a multiplier built using components from the analog blocks. Moreover, the proposed fabric yields 77% energy reduction compared to CMOS when used to implement CS reconstruction, in addition to latency improvements

Non-volatile FPGA architecture using resistive switching devices

This dissertation reports the research work that was conducted to propose a non-volatile architecture for FPGA using resistive switching devices. This is achieved by designing a Configurable Memristive Logic Block (CMLB). The CMLB comprises of memristive logic cells (MLC) interconnected to each other using memristive switch matrices. In the MLC, novel memristive D flip-flop (MDFF), 6-bit non-volatile look-up table (NVLUT), and CMOS-based multiplexers are used. Other than the MDFF, a non-volatile D-latch (NVDL) was also designed. The MDFF and the NVDL are proposed to replace CMOS-based D flip-flops and D-latches to improve energy consumption. The CMLB shows a reduction of 8.6% of device area and 1.094 times lesser critical path delay against the SRAM-based FPGA architecture. Against similar CMOS-based circuits, the MDFF provides switching speed of 1.08 times faster; the NVLUT reduces power consumption by 6.25nW and improves device area by 128 transistors; while the memristive logic cells reduce overall device area by 60.416μm2. The NVLUT is constructed using novel 2TG1M memory cells, which has the fastest switching times of 12.14ns, compared to other similar memristive memory cells. This is due to the usage of transmission gates which improves voltage transfer from input to the memristor. The novel 2TG1M memory cell also has lower energy consumption than the CMOS-based 6T SRAM cell. The memristive-based switch matrices that interconnects the MLCs together comprises of novel 7T1M SRAM cells, which has the lowest energy-delay-area-product value of 1.61 among other memristive SRAM cells. Two memristive logic gates (MLG) were also designed (OR and AND), that introduces non-volatility into conventional logic gates. All the above circuits and design simulations were performed on an enhanced SPICE memristor model, which was improved from a previously published memristor model. The previously published memristor model was fault to not be in good agreement with memristor theory and the physical model of memristors. Therefore, the enhanced SPICE memristor model provides a memristor model which is in good agreement with the memristor theory and the physical model of memristors, which is used throughout this research work

An Energy-Efficient Design Paradigm for a Memory Cell Based on Novel Nanoelectromechanical Switches

In this chapter, we explain NEMsCAM cell, a new content-addressable memory (CAM) cell, which is designed based on both CMOS technologies and nanoelectromechanical (NEM) switches. The memory part of NEMsCAM is designed with two complementary nonvolatile NEM switches and located on top of the CMOS-based comparison component. As a use case, we evaluate first-level instruction and data translation lookaside buffers (TLBs) with 16 nm CMOS technology at 2 GHz. The simulation results demonstrate that the NEMsCAM TLB reduces the energy consumption per search operation (by 27%), standby mode (by 53.9%), write operation (by 41.9%), and the area (by 40.5%) compared to a CMOS-only TLB with minimal performance overhead

Low Power Memory/Memristor Devices and Systems

This reprint focusses on achieving low-power computation using memristive devices. The topic was designed as a convenient reference point: it contains a mix of techniques starting from the fundamental manufacturing of memristive devices all the way to applications such as physically unclonable functions, and also covers perspectives on, e.g., in-memory computing, which is inextricably linked with emerging memory devices such as memristors. Finally, the reprint contains a few articles representing how other communities (from typical CMOS design to photonics) are fighting on their own fronts in the quest towards low-power computation, as a comparison with the memristor literature. We hope that readers will enjoy discovering the articles within

BOOLEAN AND BRAIN-INSPIRED COMPUTING USING SPIN-TRANSFER TORQUE DEVICES

Several completely new approaches (such as spintronic, carbon nanotube, graphene, TFETs, etc.) to information processing and data storage technologies are emerging to address the time frame beyond current Complementary Metal-Oxide-Semiconductor (CMOS) roadmap. The high speed magnetization switching of a nano-magnet due to current induced spin-transfer torque (STT) have been demonstrated in recent experiments. Such STT devices can be explored in compact, low power memory and logic design. In order to truly leverage STT devices based computing, researchers require a re-think of circuit, architecture, and computing model, since the STT devices are unlikely to be drop-in replacements for CMOS. The potential of STT devices based computing will be best realized by considering new computing models that are inherently suited to the characteristics of STT devices, and new applications that are enabled by their unique capabilities, thereby attaining performance that CMOS cannot achieve. The goal of this research is to conduct synergistic exploration in architecture, circuit and device levels for Boolean and brain-inspired computing using nanoscale STT devices. Specifically, we first show that the non-volatile STT devices can be used in designing configurable Boolean logic blocks. We propose a spin-memristor threshold logic (SMTL) gate design, where memristive cross-bar array is used to perform current mode summation of binary inputs and the low power current mode spintronic threshold device carries out the energy efficient threshold operation. Next, for brain-inspired computing, we have exploited different spin-transfer torque device structures that can implement the hard-limiting and soft-limiting artificial neuron transfer functions respectively. We apply such STT based neuron (or ‘spin-neuron’) in various neural network architectures, such as hierarchical temporal memory and feed-forward neural network, for performing “human-like” cognitive computing, which show more than two orders of lower energy consumption compared to state of the art CMOS implementation. Finally, we show the dynamics of injection locked Spin Hall Effect Spin-Torque Oscillator (SHE-STO) cluster can be exploited as a robust multi-dimensional distance metric for associative computing, image/ video analysis, etc. Our simulation results show that the proposed system architecture with injection locked SHE-STOs and the associated CMOS interface circuits can be suitable for robust and energy efficient associative computing and pattern matching

Non-volatile FPGA architecture using resistive switching devices

This dissertation reports the research work that was conducted to propose a non-volatile architecture for FPGA using resistive switching devices. This is achieved by designing a Configurable Memristive Logic Block (CMLB). The CMLB comprises of memristive logic cells (MLC) interconnected to each other using memristive switch matrices. In the MLC, novel memristive D flip-flop (MDFF), 6-bit non-volatile look-up table (NVLUT), and CMOS-based multiplexers are used. Other than the MDFF, a non-volatile D-latch (NVDL) was also designed. The MDFF and the NVDL are proposed to replace CMOS-based D flip-flops and D-latches to improve energy consumption. The CMLB shows a reduction of 8.6% of device area and 1.094 times lesser critical path delay against the SRAM-based FPGA architecture. Against similar CMOS-based circuits, the MDFF provides switching speed of 1.08 times faster; the NVLUT reduces power consumption by 6.25nW and improves device area by 128 transistors; while the memristive logic cells reduce overall device area by 60.416μm2. The NVLUT is constructed using novel 2TG1M memory cells, which has the fastest switching times of 12.14ns, compared to other similar memristive memory cells. This is due to the usage of transmission gates which improves voltage transfer from input to the memristor. The novel 2TG1M memory cell also has lower energy consumption than the CMOS-based 6T SRAM cell. The memristive-based switch matrices that interconnects the MLCs together comprises of novel 7T1M SRAM cells, which has the lowest energy-delay-area-product value of 1.61 among other memristive SRAM cells. Two memristive logic gates (MLG) were also designed (OR and AND), that introduces non-volatility into conventional logic gates. All the above circuits and design simulations were performed on an enhanced SPICE memristor model, which was improved from a previously published memristor model. The previously published memristor model was fault to not be in good agreement with memristor theory and the physical model of memristors. Therefore, the enhanced SPICE memristor model provides a memristor model which is in good agreement with the memristor theory and the physical model of memristors, which is used throughout this research work