CAD methodologies for low power and reliable 3D ICs

The main objective of this dissertation is to explore and develop computer-aided-design (CAD) methodologies and optimization techniques for reliability, timing performance, and power consumption of through-silicon-via(TSV)-based and monolithic 3D IC designs. The 3D IC technology is a promising answer to the device scaling and interconnect problems that industry faces today. Yet, since multiple dies are stacked vertically in 3D ICs, new problems arise such as thermal, power delivery, and so on. New physical design methodologies and optimization techniques should be developed to address the problems and exploit the design freedom in 3D ICs. Towards the objective, this dissertation includes four research projects. The first project is on the co-optimization of traditional design metrics and reliability metrics for 3D ICs. It is well known that heat removal and power delivery are two major reliability concerns in 3D ICs. To alleviate thermal problem, two possible solutions have been proposed: thermal-through-silicon-vias (T-TSVs) and micro-fluidic-channel (MFC) based cooling. For power delivery, a complex power distribution network is required to deliver currents reliably to all parts of the 3D IC while suppressing the power supply noise to an acceptable level. However, these thermal and power networks pose major challenges in signal routability and congestion. In this project, a co-optimization methodology for signal, power, and thermal interconnects in 3D ICs is presented. The goal of the proposed approach is to improve signal, thermal, and power noise metrics and to provide fast and accurate design space explorations for early design stages. The second project is a study on 3D IC partition. For a 3D IC, the target circuit needs to be partitioned into multiple parts then mapped onto the dies. The partition style impacts design quality such as footprint, wirelength, timing, and so on. In this project, the design methodologies of 3D ICs with different partition styles are demonstrated. For the LEON3 multi-core microprocessor, three partitioning styles are compared: core-level, block-level, and gate-level. The design methodologies for such partitioning styles and their implications on the physical layout are discussed. Then, to perform timing optimizations for 3D ICs, two timing constraint generation methods are demonstrated that lead to different design quality. The third project is on the buffer insertion for timing optimization of 3D ICs. For high performance 3D ICs, it is crucial to perform thorough timing optimizations. Among timing optimization techniques, buffer insertion is known to be the most effective way. The TSVs have a large parasitic capacitance that increases the signal slew and the delay on the downstream. In this project, a slew-aware buffer insertion algorithm is developed that handles full 3D nets and considers TSV parasitics and slew effects on delay. Compared with the well-known van Ginneken algorithm and a commercial tool, the proposed algorithm finds buffering solutions with lower delay values and acceptable runtime overhead. The last project is on the ultra-high-density logic designs for monolithic 3D ICs. The nano-scale 3D interconnects available in monolithic 3D IC technology enable ultra-high-density device integration at the individual transistor-level. The benefits and challenges of monolithic 3D integration technology for logic designs are investigated. First, a 3D standard cell library for transistor-level monolithic 3D ICs is built and their timing and power behavior are characterized. Then, various interconnect options for monolithic 3D ICs that improve design quality are explored. Next, timing-closed, full-chip GDSII layouts are built and iso-performance power comparisons with 2D IC designs are performed. Important design metrics such as area, wirelength, timing, and power consumption are compared among transistor-level monolithic 3D, gate-level monolithic 3D, TSV-based 3D, and traditional 2D designs.PhDCommittee Chair: Lim, Sung Kyu; Committee Member: Bakir, Muhannad; Committee Member: Kim, Hyesoon; Committee Member: Lee, Hsien-Hsin; Committee Member: Mukhopadhyay, Saiba

LightBox: Full-stack Protected Stateful Middlebox at Lightning Speed

Running off-site software middleboxes at third-party service providers has been a popular practice. However, routing large volumes of raw traffic, which may carry sensitive information, to a remote site for processing raises severe security concerns. Prior solutions often abstract away important factors pertinent to real-world deployment. In particular, they overlook the significance of metadata protection and stateful processing. Unprotected traffic metadata like low-level headers, size and count, can be exploited to learn supposedly encrypted application contents. Meanwhile, tracking the states of 100,000s of flows concurrently is often indispensable in production-level middleboxes deployed at real networks. We present LightBox, the first system that can drive off-site middleboxes at near-native speed with stateful processing and the most comprehensive protection to date. Built upon commodity trusted hardware, Intel SGX, LightBox is the product of our systematic investigation of how to overcome the inherent limitations of secure enclaves using domain knowledge and customization. First, we introduce an elegant virtual network interface that allows convenient access to fully protected packets at line rate without leaving the enclave, as if from the trusted source network. Second, we provide complete flow state management for efficient stateful processing, by tailoring a set of data structures and algorithms optimized for the highly constrained enclave space. Extensive evaluations demonstrate that LightBox, with all security benefits, can achieve 10Gbps packet I/O, and that with case studies on three stateful middleboxes, it can operate at near-native speed.Comment: Accepted at ACM CCS 201

A Flexible Patch-Based Lattice Boltzmann Parallelization Approach for Heterogeneous GPU-CPU Clusters

Sustaining a large fraction of single GPU performance in parallel computations is considered to be the major problem of GPU-based clusters. In this article, this topic is addressed in the context of a lattice Boltzmann flow solver that is integrated in the WaLBerla software framework. We propose a multi-GPU implementation using a block-structured MPI parallelization, suitable for load balancing and heterogeneous computations on CPUs and GPUs. The overhead required for multi-GPU simulations is discussed in detail and it is demonstrated that the kernel performance can be sustained to a large extent. With our GPU implementation, we achieve nearly perfect weak scalability on InfiniBand clusters. However, in strong scaling scenarios multi-GPUs make less efficient use of the hardware than IBM BG/P and x86 clusters. Hence, a cost analysis must determine the best course of action for a particular simulation task. Additionally, weak scaling results of heterogeneous simulations conducted on CPUs and GPUs simultaneously are presented using clusters equipped with varying node configurations.Comment: 20 pages, 12 figure

Automated Debugging Methodology for FPGA-based Systems

Electronic devices make up a vital part of our lives. These are seen from mobiles, laptops, computers, home automation, etc. to name a few. The modern designs constitute billions of transistors. However, with this evolution, ensuring that the devices fulfill the designer’s expectation under variable conditions has also become a great challenge. This requires a lot of design time and effort. Whenever an error is encountered, the process is re-started. Hence, it is desired to minimize the number of spins required to achieve an error-free product, as each spin results in loss of time and effort. Software-based simulation systems present the main technique to ensure the verification of the design before fabrication. However, few design errors (bugs) are likely to escape the simulation process. Such bugs subsequently appear during the post-silicon phase. Finding such bugs is time-consuming due to inherent invisibility of the hardware. Instead of software simulation of the design in the pre-silicon phase, post-silicon techniques permit the designers to verify the functionality through the physical implementations of the design. The main benefit of the methodology is that the implemented design in the post-silicon phase runs many order-of-magnitude faster than its counterpart in pre-silicon. This allows the designers to validate their design more exhaustively. This thesis presents five main contributions to enable a fast and automated debugging solution for reconfigurable hardware. During the research work, we used an obstacle avoidance system for robotic vehicles as a use case to illustrate how to apply the proposed debugging solution in practical environments. The first contribution presents a debugging system capable of providing a lossless trace of debugging data which permits a cycle-accurate replay. This methodology ensures capturing permanent as well as intermittent errors in the implemented design. The contribution also describes a solution to enhance hardware observability. It is proposed to utilize processor-configurable concentration networks, employ debug data compression to transmit the data more efficiently, and partially reconfiguring the debugging system at run-time to save the time required for design re-compilation as well as preserve the timing closure. The second contribution presents a solution for communication-centric designs. Furthermore, solutions for designs with multi-clock domains are also discussed. The third contribution presents a priority-based signal selection methodology to identify the signals which can be more helpful during the debugging process. A connectivity generation tool is also presented which can map the identified signals to the debugging system. The fourth contribution presents an automated error detection solution which can help in capturing the permanent as well as intermittent errors without continuous monitoring of debugging data. The proposed solution works for designs even in the absence of golden reference. The fifth contribution proposes to use artificial intelligence for post-silicon debugging. We presented a novel idea of using a recurrent neural network for debugging when a golden reference is present for training the network. Furthermore, the idea was also extended to designs where golden reference is not present