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

SPRING: A Sparsity-Aware Reduced-Precision Monolithic 3D CNN Accelerator Architecture for Training and Inference

CNNs outperform traditional machine learning algorithms across a wide range of applications. However, their computational complexity makes it necessary to design efficient hardware accelerators. Most CNN accelerators focus on exploring dataflow styles that exploit computational parallelism. However, potential performance speedup from sparsity has not been adequately addressed. The computation and memory footprint of CNNs can be significantly reduced if sparsity is exploited in network evaluations. To take advantage of sparsity, some accelerator designs explore sparsity encoding and evaluation on CNN accelerators. However, sparsity encoding is just performed on activation or weight and only in inference. It has been shown that activation and weight also have high sparsity levels during training. Hence, sparsity-aware computation should also be considered in training. To further improve performance and energy efficiency, some accelerators evaluate CNNs with limited precision. However, this is limited to the inference since reduced precision sacrifices network accuracy if used in training. In addition, CNN evaluation is usually memory-intensive, especially in training. In this paper, we propose SPRING, a SParsity-aware Reduced-precision Monolithic 3D CNN accelerator for trainING and inference. SPRING supports both CNN training and inference. It uses a binary mask scheme to encode sparsities in activation and weight. It uses the stochastic rounding algorithm to train CNNs with reduced precision without accuracy loss. To alleviate the memory bottleneck in CNN evaluation, especially in training, SPRING uses an efficient monolithic 3D NVM interface to increase memory bandwidth. Compared to GTX 1080 Ti, SPRING achieves 15.6X, 4.2X and 66.0X improvements in performance, power reduction, and energy efficiency, respectively, for CNN training, and 15.5X, 4.5X and 69.1X improvements for inference

LOW POWER CIRCUITS DESIGN USING RESISTIVE NON-VOLATILE MEMORIES

Ph.DDOCTOR OF PHILOSOPH

Signaling in 3-D integrated circuits, benefits and challenges

Three-dimensional (3-D) or vertical integration is a design and packaging paradigm that can mitigate many of the increasing challenges related to the design of modern integrated systems. 3-D circuits have recently been at the spotlight, since these circuits provide a potent approach to enhance the performance and integrate diverse functions within amulti-plane stack. Clock networks consume a great portion of the power dissipated in a circuit. Therefore, designing a low-power clock network in synchronous circuits is an important task. This requirement is stricter for 3-D circuits due to the increased power densities. Synchronization issues can be more challenging for 3-D circuits since a clock path can spread across several planes with different physical and electrical characteristics. Consequently, designing low power clock networks for 3-D circuits is an important issue. Resonant clock networks are considered efficient low-power alternatives to conventional clock distribution schemes. These networks utilize additional inductive circuits to reduce power while delivering a full swing clock signal to the sink nodes. In this research, a design method to apply resonant clocking to synthesized clock trees is proposed. Manufacturing processes for 3-D circuits include some additional steps as compared to standard CMOS processes which makes 3-D circuits more susceptible to manufacturing defects and lowers the overall yield of the bonded 3-D stack. Testing is another complicated task for 3-D ICs, where pre-bond test is a prerequisite. Pre-bond testability, in turn, presents new challenges to 3-D clock network design primarily due to the incomplete clock distribution networks prior to the bonding of the planes. A design methodology of resonant 3-D clock networks that support wireless pre-bond testing is introduced. To efficiently address this issue, inductive links are exploited to wirelessly transmit the clock signal to the disjoint resonant clock networks. The inductors comprising the LC tanks are used as the receiver circuit for the links, essentially eliminating the need for additional circuits and/or interconnect resources during pre-bond test. Recent FPGAs are quite complex circuits which provide reconfigurablity at the cost of lower performance and higher power consumption as compared to ASIC circuits. Exploiting a large number of programmable switches, routing structures are mainly responsible for performance degradation in FPAGs. Employing 3-D technology can providemore efficient switches which drastically improve the performance and reduce the power consumption of the FPGA. RRAM switches are one of the most promising candidates to improve the FPGA routing architecture thanks to their low on-resistance and non-volatility. Along with the configurable switches, buffers are the other important element of the FPGAs routing structure. Different characteristics of RRAM switches change the properties of signal paths in RRAM-based FPGAs. The on resistance of RRAMswitches is considerably lower than CMOS pass gate switches which results in lower RC delay for RRAM-based routing paths. This different nature in critical path and signal delay in turn affect the need for intermediate buffers. Thus the buffer allocation should be reconsidered. In the last part of this research, the effect of intermediate buffers on signal propagation delay is studied and a modified buffer allocation scheme for RRAM-based FPGA routing path is proposed

A Ultra-Low-Power FPGA Based on Monolithically Integrated RRAMs (invited)

Field Programmable Gate Arrays (FPGAs) rely heavily on complex routing architectures. The routing structures use programmable switches and account for a significant share in the total area, delay and power consumption numbers. With the ability of being monolithically integrated with CMOS chips, Resistive Random Access Memories (RRAMs) enable high-performance routing architectures through the replacement of Static Random Access Memory (SRAM)-based programming switches. Exploiting the very low on-resistance state achievable by RRAMs as well as the improved tolerance to power supply reduction, RRAM-based routing multiplexers can be used to significantly reduce the power consumption of FPGA systems with no performance compromises. By evaluating the opportunities of ultra-low-power RRAM-based FPGAs at the system level, we see an improvement of 12%, 26% and 81% in area, delay and power consumption at a mature technology node

Accurate Power Analysis for Near-Vt RRAM-based FPGA

Resistive Random Access Memory (RRAM)-based FPGA architectures employ RRAMs not only as memories to store the configuration but embed them in the datapaths of programmable routing resources to propagate signals with improved performances. Sources of power consumption have been intensively studied for conventional Static Random Access Memories (SRAM)-based FPGAs. However, very limited works focused so far on studying the power characteristics of RRAM-based FPGAs. In this paper, we first analyze the power characteristics of RRAM-based multiplexer at circuit level and then use electrical simulations to study power consumption of RRAM-based FPGA architectures. Experimental results show that RRAM-based FPGAs achieve a Power-Delay Product reduced by 50% compared to SRAM-based FPGA at nominal voltage and 20% compared to near-Vt SRAM-based FPGA, respectively

A Construction Kit for Efficient Low Power Neural Network Accelerator Designs

Implementing embedded neural network processing at the edge requires efficient hardware acceleration that couples high computational performance with low power consumption. Driven by the rapid evolution of network architectures and their algorithmic features, accelerator designs are constantly updated and improved. To evaluate and compare hardware design choices, designers can refer to a myriad of accelerator implementations in the literature. Surveys provide an overview of these works but are often limited to system-level and benchmark-specific performance metrics, making it difficult to quantitatively compare the individual effect of each utilized optimization technique. This complicates the evaluation of optimizations for new accelerator designs, slowing-down the research progress. This work provides a survey of neural network accelerator optimization approaches that have been used in recent works and reports their individual effects on edge processing performance. It presents the list of optimizations and their quantitative effects as a construction kit, allowing to assess the design choices for each building block separately. Reported optimizations range from up to 10'000x memory savings to 33x energy reductions, providing chip designers an overview of design choices for implementing efficient low power neural network accelerators

Hardware Acceleration for Tensorized Neural Networks

Machine learning has gained success in many application domains including medical data analysis, finance, computer vision, and so forth. However, many popular machine learning models (e.g., deep neural networks) are both data-intensive and computationally expensive: they require high-volume data samples to train the networks, millions to billions of parameters to describe the model, and large-scale computations to complete the optimization or inference. Therefore, deep learning can cause unaffordable energy and run-time cost on a hardware platform. In this paper, we present a way of accelerating deep neural networks as well as compressing weights used by designing hardware acceleration for tensor train decomposition layers in deep neural networks. By utilizing hardware acceleration on tensorized neural networks, we achieved massive memory saving on two fully -connected layers. Parameters shrink 4880644x and 3195660x respectively. At the same time, we achieve speed up at 2600x and 2900x compared to original matrix multiplication process