Transition-Aware Decoupling-Capacitor Allocation in Power Noise Reduction

Abstract-Dynamic power noises may not only degrade the circuit performance but also reduce the noise margin which may result in the functional errors in integrated circuit. Decoupling capacitor (decap) allocation is one of the most effective way in reducing serious dynamic power noises (hotspots). To allocate decap bef ore placement, we observed that not only locations but also rising time of functional cells are required to accurately predict power noises. Compared to a previous work which only takes neighborhood relation into consideration, our method is more efficient in reducing hotspots. Furthermore, to reduce the hotspots af ter placement, instead of only using the empty space as proposed in the previous work, we move out cells in the area with serious power noise area (hot area). The obtained empty space can be used to accommodate decaps to further reduce the hotspots. The experimental result shows, compared to the previous work [1], our estimation function to allocate decap before placement is 23% better in reducing power noises. Moreover, compared to a method which fills decaps to all remaining empty space, our cell move algorithm can almost eliminate all the remaining hot grid nodes and hot cells. In summary, compared to the original circuits (without decap), about 60% of hotspots can be removed using our prediction function before placement, and most of the remaining hotspots are removed by our cell moving step after placement

Applications of river formation dynamics

River formation dynamics is a metaheuristic where solutions are constructed by iteratively modifying the values associated to the nodes of a graph. Its gradient orientation provides interesting features such as the fast reinforcement of new shortcuts, the natural avoidance of cycles, and the focused elimination of blind alleys. Since the method was firstly proposed in 2007, several research groups have applied it to a wide variety of application domains, such as telecommunications, software testing, industrial manufacturing processes, or navigation. In this paper we review the main works of the last decade where the river formation dynamics metaheuristic has been applied to solve optimization problems

Parallel Simulation for VLSI Power Grid

Due to the increasing complexity of VLSI circuits, power grid simulation has become more and more time-consuming. Hence, there is a need for fast and accurate power grid simulator. In order to perform power grid simulation in a timely manner, parallel algorithms have been developed to accelerate the simulation. In this dissertation, we present parallel algorithms and software for power grid simulation on CPU-GPU platforms. The power grid is divided into disjoint partitions. The partitions are enlarged using Breath First Search (BFS) method. In the partition enlarging process, a portion of edges are ignored to make the matrix factorization light-weight. Solving the enlarged partitions using a direct solver serves as a preconditioner for the Preconditioned Conjugate Gradient (PCG) method that is used to solve the power grid. This work combines the advantages of direct solvers and iterative solvers to obtain an efficient hybrid parallel solver. Two-tier parallelism is harnessed using MPI for partitions and CUDA within each partition. The experiments conducted on supercomputing clusters demonstrate significant speed improvements over a state-of-the-art direct solver in both static and transient analysis

Scalable Analysis, Verification and Design of IC Power Delivery

Due to recent aggressive process scaling into the nanometer regime, power delivery network design faces many challenges that set more stringent and specific requirements to the EDA tools. For example, from the perspective of analysis, simulation efficiency for large grids must be improved and the entire network with off-chip models and nonlinear devices should be able to be analyzed. Gated power delivery networks have multiple on/off operating conditions that need to be fully verified against the design requirements. Good power delivery network designs not only have to save the wiring resources for signal routing, but also need to have the optimal parameters assigned to various system components such as decaps, voltage regulators and converters. This dissertation presents new methodologies to address these challenging problems. At first, a novel parallel partitioning-based approach which provides a flexible network partitioning scheme using locality is proposed for power grid static analysis. In addition, a fast CPU-GPU combined analysis engine that adopts a boundary-relaxation method to encompass several simulation strategies is developed to simulate power delivery networks with off-chip models and active circuits. These two proposed analysis approaches can achieve scalable simulation runtime. Then, for gated power delivery networks, the challenge brought by the large verification space is addressed by developing a strategy that efficiently identifies a number of candidates for the worst-case operating condition. The computation complexity is reduced from O(2^N) to O(N). At last, motivated by a proposed two-level hierarchical optimization, this dissertation presents a novel locality-driven partitioning scheme to facilitate divide-and-conquer-based scalable wire sizing for large power delivery networks. Simultaneous sizing of multiple partitions is allowed which leads to substantial runtime improvement. Moreover, the electric interactions between active regulators/converters and passive networks and their influences on key system design specifications are analyzed comprehensively. With the derived design insights, the system-level co-design of a complete power delivery network is facilitated by an automatic optimization flow. Results show significant performance enhancement brought by the co-design

Modeling and Analysis of Large-Scale On-Chip Interconnects

As IC technologies scale to the nanometer regime, efficient and accurate modeling and analysis of VLSI systems with billions of transistors and interconnects becomes increasingly critical and difficult. VLSI systems impacted by the increasingly high dimensional process-voltage-temperature (PVT) variations demand much more modeling and analysis efforts than ever before, while the analysis of large scale on-chip interconnects that requires solving tens of millions of unknowns imposes great challenges in computer aided design areas. This dissertation presents new methodologies for addressing the above two important challenging issues for large scale on-chip interconnect modeling and analysis: In the past, the standard statistical circuit modeling techniques usually employ principal component analysis (PCA) and its variants to reduce the parameter dimensionality. Although widely adopted, these techniques can be very limited since parameter dimension reduction is achieved by merely considering the statistical distributions of the controlling parameters but neglecting the important correspondence between these parameters and the circuit performances (responses) under modeling. This dissertation presents a variety of performance-oriented parameter dimension reduction methods that can lead to more than one order of magnitude parameter reduction for a variety of VLSI circuit modeling and analysis problems. The sheer size of present day power/ground distribution networks makes their analysis and verification tasks extremely runtime and memory inefficient, and at the same time, limits the extent to which these networks can be optimized. Given today?s commodity graphics processing units (GPUs) that can deliver more than 500 GFlops (Flops: floating point operations per second). computing power and 100GB/s memory bandwidth, which are more than 10X greater than offered by modern day general-purpose quad-core microprocessors, it is very desirable to convert the impressive GPU computing power to usable design automation tools for VLSI verification. In this dissertation, for the first time, we show how to exploit recent massively parallel single-instruction multiple-thread (SIMT) based graphics processing unit (GPU) platforms to tackle power grid analysis with very promising performance. Our GPU based network analyzer is capable of solving tens of millions of power grid nodes in just a few seconds. Additionally, with the above GPU based simulation framework, more challenging three-dimensional full-chip thermal analysis can be solved in a much more efficient way than ever before

Modeling and Design Techniques for 3-D ICs under Process, Voltage, and Temperature Variations

Three-dimensional (3-D) integration is a promising solution to further enhance the density and performance of modern integrated circuits (ICs). In 3-D ICs, multiple dies (tiers or planes) are vertically stacked. These dies can be designed and fabricated separately. In addition, these dies can be fabricated in different technologies. The effect of different sources of variations on 3-D circuits, consequently, differ from 2-D ICs. As technology scales, these variations significantly affect the performance of circuits. Therefore, it is increasingly important to accurately and efficiently model different sources of variations in 3-D ICs. The process, voltage, and temperature variations in 3-D ICs are investigated in this dissertation. Related modeling and design techniques are proposed to design a robust 3-D IC. Process variations in 3-D ICs are first analyzed. The effect of process variations on synchronization and 3-D clock distribution networks, is carefully studied. A novel statistical model is proposed to describe the timing variation in 3-D clock distribution networks caused by process variations. Based on this model, different topologies of 3-D clock distribution networks are compared in terms of skew variation. A set of guidelines is proposed to design 3-D clock distribution networks with low clock uncertainty. Voltage variations are described by power supply noise. Power supply noise in 3-D ICs is investigated considering different characteristics of potential 3-D power grids in this thesis. A new algorithm is developed to fast analyze the steady-state IR-drop in 3-D power grids. The first droop of power supply noise, also called resonant supply noise, is usually the deepest voltage drop in power distribution networks. The effect of resonant supply noise on 3-D clock distribution networks is investigated. The combined effect of process variations and power supply noise is modeled by skitter consisting of both skew and jitter. A novel statistical model of skitter is proposed. Based on this proposed model and simulation results, a set of guidelines has been proposed to mitigate the negative effect of process and voltage variations on 3-D clock distribution networks. Thermal issues in 3-D ICs are considered by carefully modeling thermal through silicon vias (TTSVs) in this dissertation. TTSVs are vertical vias which do not carry signals, dedicated to facilitate the propagation of heat to reduce the temperature of 3-D ICs. Two analytic models are proposed to describe the heat transfer in 3-D circuits related to TTSVs herein, providing proper closed-form expressions for the thermal resistance of the TTSVs. The effect of different physical and geometric parameters of TTSVs on the temperature of 3-D ICs is analyzed. The proposed models can be used to fast and accurately estimate the temperature to avoid the overuse of TTSVs occupying a large portion of area. A set of models and design techniques is proposed in this dissertation to describe and mitigate the deleterious effects of process, voltage, and temperature variations in 3-D ICs. Due to the continuous shrink in the feature size of transistors, the large number of devices within one circuit, and the high operating frequency, the effect of these variations on the performance of 3-D ICs becomes increasingly significant. Accurately and efficiently estimating and controlling these variations are, consequently, critical tasks for the design of 3-D ICs