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Nanometer VLSI placement and optimization for multi-objective design closure
In a VLSI physical synthesis flow, placement directly defines the interconnection,
which affects many other design objectives, such as timing, power consumption,
congestion, and thermal issues. With the scaling of technology, the relative interconnect
delay increases dramatically. As a result, placement has become a bottleneck
in deep sub-micron physical synthesis. In this dissertation, I propose several
optimization algorithms from global placement, placement migration, timing driven
placements, to incremental power optimizations for multi-objective VLSI design
closure. The first work is DPlace, a new global placement algorithm that scales
well to the modern large-scale circuit placement problems. DPlace simulates the
natural diffusion process to spread cells smoothly over the placement region, and
uses both analytical and discrete techniques to improve the wire length. However,
global placement is never sufficient for multi-objective design closure, a variety of
design objectives have to be improved incrementally, such as timing, routing congestion,
signal integrity, and heat distribution. Placement migration is a critical step
to address the cell overlaps appearing during incremental optimizations. To achieve
high placement stability, I propose a computational geometry based placement migration
flow to cope with placement changes, and a new stability metric to measure
the âsimilarityâ between two placements accurately. Our placement migration algorithm
has clear advantage over conventional legalization algorithms such that the
neighborhood characteristics of the original placement are preserved. For timing
closure in high performance designs, I present a linear programming based incremental
timing driven placement to improve the timing on critical paths directly.
I further present an efficient timing driven placement algorithm (Pyramids). Two
formulations of Pyramids are proposed, which are suitable for different optimization
stages in a physical synthesis flow. Both approaches find the optimal location
for timing of a cell in constant time, through computational geometry based approaches.
For fast convergence of design closure, placement should be integrated
with other optimization techniques. I propose to combine placement, gate sizing
and Vt swapping techniques to reduce the total power consumption, especially the
leakage power, which is becoming increasingly critical for nanometer VLSI design
closure.Electrical and Computer Engineerin
A framework for fine-grain synthesis optimization of operational amplifiers
This thesis presents a cell-level framework for Operational Amplifiers Synthesis (OASYN) coupling both circuit design and layout. For circuit design, the tool applies a corner-driven optimization, accounting for on-chip performance variations. By exploring the process, voltage, and temperature variations space, the tool extracts design worst case solution. The tool undergoes sensitivity analysis along with Pareto-optimality to achieve required specifications. For layout phase, OASYN generates a DRC proved automated layout based on a sized circuit-level description. Morata et al. (1996) introduced an elegant representation of block placement called sequence pair for general floorplans (SP). Like TCG and BSG, but unlike O-tree, B*tree, and CBL, SP is P-admissible. Unlike SP, TCG supports incremental update during operation and keeps the information of the boundary modules as well as their relative positions in the representation. Block placement algorithms that are based on SP use heuristic optimization algorithms, e.g., simulated annealing where generation of large number of sequence pairs are required. Therefore a fast algorithm is needed to generate sequence pairs after each solution perturbation. The thesis presents a new simple and efficient O(n) runtime algorithm for fast realization of incremental update for cost evaluation. The algorithm integrates sequence pair and transitive closure graph advantages into TCG-S* a superior topology update scheme which facilitates the search for optimum desired floorplan. Experiments show that TCG-S* is better than existing works in terms of area utilization and convergence speed. Routing-aware placement is implemented in OASYN, handling symmetry constraints, e.g., interdigitization, common centroid, along with congestion elimination and the enhancement of placement routability
Tight coupling of timing driven placement and retiming
Retiming is a widely investigated technique for performance optimization. In general, it performs extensive modifications on a circuit netlist, leaving it unclear, whether the achieved performance improvement will still be valid after placement has been performed. This paper presents an approach for integrating retiming into a timing-driven placement environment. The experimental results show the benefit of the proposed approach on circuit performance in comparison with design flows using retiming only as a pre- or postplacement optimization method
Online VNF Scaling in Datacenters
Network Function Virtualization (NFV) is a promising technology that promises
to significantly reduce the operational costs of network services by deploying
virtualized network functions (VNFs) to commodity servers in place of dedicated
hardware middleboxes. The VNFs are typically running on virtual machine
instances in a cloud infrastructure, where the virtualization technology
enables dynamic provisioning of VNF instances, to process the fluctuating
traffic that needs to go through the network functions in a network service. In
this paper, we target dynamic provisioning of enterprise network services -
expressed as one or multiple service chains - in cloud datacenters, and design
efficient online algorithms without requiring any information on future traffic
rates. The key is to decide the number of instances of each VNF type to
provision at each time, taking into consideration the server resource
capacities and traffic rates between adjacent VNFs in a service chain. In the
case of a single service chain, we discover an elegant structure of the problem
and design an efficient randomized algorithm achieving a e/(e-1) competitive
ratio. For multiple concurrent service chains, an online heuristic algorithm is
proposed, which is O(1)-competitive. We demonstrate the effectiveness of our
algorithms using solid theoretical analysis and trace-driven simulations.Comment: 9 pages, 4 figure
ActiveRemediation: The Search for Lead Pipes in Flint, Michigan
We detail our ongoing work in Flint, Michigan to detect pipes made of lead
and other hazardous metals. After elevated levels of lead were detected in
residents' drinking water, followed by an increase in blood lead levels in area
children, the state and federal governments directed over $125 million to
replace water service lines, the pipes connecting each home to the water
system. In the absence of accurate records, and with the high cost of
determining buried pipe materials, we put forth a number of predictive and
procedural tools to aid in the search and removal of lead infrastructure.
Alongside these statistical and machine learning approaches, we describe our
interactions with government officials in recommending homes for both
inspection and replacement, with a focus on the statistical model that adapts
to incoming information. Finally, in light of discussions about increased
spending on infrastructure development by the federal government, we explore
how our approach generalizes beyond Flint to other municipalities nationwide.Comment: 10 pages, 10 figures, To appear in KDD 2018, For associated
promotional video, see https://www.youtube.com/watch?v=YbIn_axYu9
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