Via-switch FPGA with transistor-free programmability enabling energy-efficient near-memory parallel computation

We are developing field-programmable gate arrays (FPGAs) with a new non-volatile switch called via-switch. In via-switch FPGAs (VS-FPGAs), the via-switches required for reconfiguration are placed in the routing layer so that the entire transistor layer can be utilized for computing, and higher implementation density can be achieved compared to conventional SRAM FPGAs. Furthermore, since arithmetic units and memories for computing can be placed under the via-switch crossbar for routing, large-scale parallel operations can be realized where the memory and the arithmetic unit are adjacent to each other. These features enable operation with high energy efficiency. This article reports 65 nm prototype fabrication results and predicted the performance of the VS-FPGA designed for AI applications. We also present the developed application mapping flow and crossbar programming method. The VS-FPGA closes the gap between FPGA and application-specific integrated circuits (ASIC) with the performance advantage of the via-switch and via-switch copy scheme for FPGA-to-ASIC migration, contributing to the expansion of the FPGA usage

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

Optimization Opportunities in RRAM-based FPGA Architectures

Static Random Access Memory (SRAM)-based routing multiplexers, whatever structure is employed, share a common limitation: their area, delay and power increase linearly with the input size. This property results in most SRAM-based FPGA architectures typically avoiding the use of large multiplexers. Resistive Random Access Memory (RRAM)- based multiplexers, built with one-level structure, have a unique advantage over SRAM-based multiplexers: their ideal delay is independent from the input size. This property allows RRAM-based FPGA architectures to use larger multiplexers than their SRAM-based counterparts, without generating any delay overhead. In this paper, by carefully considering the properties of RRAM multiplexers, we assess that current state-of-art architectural parameters for SRAM-based FPGAs cannot preserve optimality in the context of RRAM-based FPGAs. As a result, we propose that in RRAM-based FPGAs, (a) the routing tracks should be interconnected to Look-Up Table (LUT) inputs via a one-level crossbar, instead of through Connection Blocks and local routing; (b) the Switch Blocks should employ larger multiplexers; (c) length-2 wires should be used instead of length-4 wires. When operated in nominal voltage, the proposed RRAM-based FPGA architecture reduces area by 26%, delay by 39% and channel width by 13%, as compared to a SRAM-based FPGA with a classical architecture. When operated in the near-Vt regime, the proposed RRAM-based FPGA architecture improves Area-Delay Product by 42% and Power-Delay Product by 5x as compared to a classical SRAM-based FPGA at nominal voltage

LOW POWER CIRCUITS DESIGN USING RESISTIVE NON-VOLATILE MEMORIES

Ph.DDOCTOR OF PHILOSOPH

Virtualizing Reconfigurable Architectures: From Fpgas To Beyond

With field-programmable gate arrays (FPGAs) being widely deployed in data centers to enhance the computing performance, an efficient virtualization support is required to fully unleash the potential of cloud FPGAs. However, the system support for FPGAs in the context of the cloud environment is still in its infancy, which leads to a low resource utilization due to the tight coupling between compilation and resource allocation. Moreover, the system support proposed in existing works is limited to a homogeneous FPGA cluster comprising identical FPGA devices, which is hard to be extended to a heterogeneous FPGA cluster that comprises multiple types of FPGAs. As the FPGA cloud is expected to become increasingly heterogeneous due to the hardware rolling upgrade strategy, it is necessary to provide efficient virtualization support for the heterogeneous FPGA cluster. In this dissertation, we first identify three pairs of conflicting requirements from runtime management and offline compilation, which are related to the tradeoff between flexibility and efficiency. These conflicting requirements are the fundamental reason why the single-level abstraction proposed in prior works for the homogeneous FPGA cluster cannot be trivially extended to the heterogeneous cluster. To decouple these conflicting requirements, we provide a two-level system abstraction. Specifically, the high-level abstraction is FPGA-agnostic and provides a simple and homogeneous view of the FPGA resources to simplify the runtime management and maximize the flexibility. On the contrary, the low-level abstraction is FPGA-specific and exposes sufficient low-level hardware details to the compilation framework to ensure the mapping quality and maximize the efficiency. This generic two-level system abstraction can also be specialized to the homogeneous FPGA cluster and/or be extended to leverage application-specific information to further improve the efficiency. We also develop a compilation framework and a modular runtime system with a heuristic-based runtime management policy to support this two-level system abstraction. By enabling a dynamic FPGA sharing at the sub-FPGA granularity, the proposed virtualization solution can deploy 1.62x more applications using the same amount of FPGA resources and reduce the compilation time by 22.6% (perform as many compilation tasks in parallel as possible) with an acceptable virtualization overhead, i.e., Finally, we use Liquid Silicon as a case study to show that the proposed virtualization solution can be extended to other spatial reconfigurable architectures. Liquid Silicon is a homogeneous reconfigurable architecture enabled by the non-volatile memory technology (i.e., RRAM). It extends the configuration capability of existing FPGAs from computation to the whole spectrum ranging from computation to data storage. It allows users to better customize hardware by flexibly partitioning hardware resources between computation and memory based on the actual usage. Instead of naively applying the proposed virtualization solution onto Liquid Silicon, we co-optimize the system abstraction and Liquid Silicon architecture to improve the performance

Emerging Run-Time Reconfigurable FPGA and CAD Tools

Field-programmable gate array (FPGA) is a post fabrication reconfigurable device to accelerate domain specific computing systems. It offers offer high operation speed and low power consumption. However, the design flexibility and performance of FPGAs are severely constrained by the costly on-chip memories, e.g. static random access memory (SRAM) and FLASH memory. The objective of my dissertation is to explore the opportunity and enable the use of the emerging resistance random access memory (ReRAM) in FPGA design. The emerging ReRAM technology features high storage density, low access power consumption, and CMOS compatibility, making it a promising candidate for FPGA implementation. In particular, ReRAM has advantages of the fast access and nonvolatility, enabling the on-chip storage and access of configuration data. In this dissertation, I first propose a novel three-dimensional stacking scheme, namely, high-density interleaved memory (HIM). The structure improves the density of ReRAM meanwhile effectively reducing the signal interference induced by sneak paths in crossbar arrays. To further enhance the access speed and design reliability, a fast sensing circuit is also presented which includes a new sense amplifier scheme and reference cell configuration. The proposed ReRAM FPGA leverages a similar architecture as conventional SRAM based FPGAs but utilizes ReRAM technology in all component designs. First, HIM is used to implement look-up table (LUT) and block random access memories (BRAMs) for func- tionality process. Second, a 2R1T, two ReRAM cells and one transistor, nonvolatile switch design is applied to construct connection blocks (CBs) and switch blocks (SBs) for signal transition. Furthermore, unified BRAM (uBRAM) based on the current BRAM architecture iv is introduced, offering both configuration and temporary data storage. The uBRAMs provides extremely high density effectively and enlarges the FPGA capacity, potentially saving multiple contexts of configuration. The fast configuration scheme from uBRAM to logic and routing components also makes fast run-time partial reconfiguration (PR) much easier, improving the flexibility and performance of the entire FPGA system. Finally, modern place and route tools are designed for homogeneous fabric of FPGA. The PR feature, however, requires the support of heterogeneous logic modules in order to differentiate PR modules from static ones and therefore maintain the signal integration. The existing approaches still reply on designers’ manual effort, which significantly prolongs design time and lowers design efficiency. In this dissertation, I integrate PR support into VPR – an academic place and route tool by introducing a B*-tree modular placer (BMP) and PR-aware router. As such, users are able to explore new architectures or map PR applications to a variety of FPGAs. More importantly, this enhanced feature can also support fast design automation, e.g. mapping IP core, loading pre-synthesizing logic modules, etc

Semiconductor Memory Applications in Radiation Environment, Hardware Security and Machine Learning System

abstract: Semiconductor memory is a key component of the computing systems. Beyond the conventional memory and data storage applications, in this dissertation, both mainstream and eNVM memory technologies are explored for radiation environment, hardware security system and machine learning applications. In the radiation environment, e.g. aerospace, the memory devices face different energetic particles. The strike of these energetic particles can generate electron-hole pairs (directly or indirectly) as they pass through the semiconductor device, resulting in photo-induced current, and may change the memory state. First, the trend of radiation effects of the mainstream memory technologies with technology node scaling is reviewed. Then, single event effects of the oxide based resistive switching random memory (RRAM), one of eNVM technologies, is investigated from the circuit-level to the system level. Physical Unclonable Function (PUF) has been widely investigated as a promising hardware security primitive, which employs the inherent randomness in a physical system (e.g. the intrinsic semiconductor manufacturing variability). In the dissertation, two RRAM-based PUF implementations are proposed for cryptographic key generation (weak PUF) and device authentication (strong PUF), respectively. The performance of the RRAM PUFs are evaluated with experiment and simulation. The impact of non-ideal circuit effects on the performance of the PUFs is also investigated and optimization strategies are proposed to solve the non-ideal effects. Besides, the security resistance against modeling and machine learning attacks is analyzed as well. Deep neural networks (DNNs) have shown remarkable improvements in various intelligent applications such as image classification, speech classification and object localization and detection. Increasing efforts have been devoted to develop hardware accelerators. In this dissertation, two types of compute-in-memory (CIM) based hardware accelerator designs with SRAM and eNVM technologies are proposed for two binary neural networks, i.e. hybrid BNN (HBNN) and XNOR-BNN, respectively, which are explored for the hardware resource-limited platforms, e.g. edge devices.. These designs feature with high the throughput, scalability, low latency and high energy efficiency. Finally, we have successfully taped-out and validated the proposed designs with SRAM technology in TSMC 65 nm. Overall, this dissertation paves the paths for memory technologies’ new applications towards the secure and energy-efficient artificial intelligence system.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201