7,399 research outputs found
Visual Question Answering with Memory-Augmented Networks
In this paper, we exploit a memory-augmented neural network to predict
accurate answers to visual questions, even when those answers occur rarely in
the training set. The memory network incorporates both internal and external
memory blocks and selectively pays attention to each training exemplar. We show
that memory-augmented neural networks are able to maintain a relatively
long-term memory of scarce training exemplars, which is important for visual
question answering due to the heavy-tailed distribution of answers in a general
VQA setting. Experimental results on two large-scale benchmark datasets show
the favorable performance of the proposed algorithm with a comparison to state
of the art.Comment: CVPR 201
TAG: Learning Circuit Spatial Embedding From Layouts
Analog and mixed-signal (AMS) circuit designs still rely on human design
expertise. Machine learning has been assisting circuit design automation by
replacing human experience with artificial intelligence. This paper presents
TAG, a new paradigm of learning the circuit representation from layouts
leveraging text, self-attention and graph. The embedding network model learns
spatial information without manual labeling. We introduce text embedding and a
self-attention mechanism to AMS circuit learning. Experimental results
demonstrate the ability to predict layout distances between instances with
industrial FinFET technology benchmarks. The effectiveness of the circuit
representation is verified by showing the transferability to three other
learning tasks with limited data in the case studies: layout matching
prediction, wirelength estimation, and net parasitic capacitance prediction.Comment: Accepted by ICCAD 202
Quantivine: A Visualization Approach for Large-scale Quantum Circuit Representation and Analysis
Quantum computing is a rapidly evolving field that enables exponential
speed-up over classical algorithms. At the heart of this revolutionary
technology are quantum circuits, which serve as vital tools for implementing,
analyzing, and optimizing quantum algorithms. Recent advancements in quantum
computing and the increasing capability of quantum devices have led to the
development of more complex quantum circuits. However, traditional quantum
circuit diagrams suffer from scalability and readability issues, which limit
the efficiency of analysis and optimization processes. In this research, we
propose a novel visualization approach for large-scale quantum circuits by
adopting semantic analysis to facilitate the comprehension of quantum circuits.
We first exploit meta-data and semantic information extracted from the
underlying code of quantum circuits to create component segmentations and
pattern abstractions, allowing for easier wrangling of massive circuit
diagrams. We then develop Quantivine, an interactive system for exploring and
understanding quantum circuits. A series of novel circuit visualizations are
designed to uncover contextual details such as qubit provenance, parallelism,
and entanglement. The effectiveness of Quantivine is demonstrated through two
usage scenarios of quantum circuits with up to 100 qubits and a formal user
evaluation with quantum experts. A free copy of this paper and all supplemental
materials are available at
https://osf.io/2m9yh/?view_only=0aa1618c97244f5093cd7ce15f1431f9.Comment: Accepted by IEEE VIS 202
What-and-Where to Match: Deep Spatially Multiplicative Integration Networks for Person Re-identification
Matching pedestrians across disjoint camera views, known as person
re-identification (re-id), is a challenging problem that is of importance to
visual recognition and surveillance. Most existing methods exploit local
regions within spatial manipulation to perform matching in local
correspondence. However, they essentially extract \emph{fixed} representations
from pre-divided regions for each image and perform matching based on the
extracted representation subsequently. For models in this pipeline, local finer
patterns that are crucial to distinguish positive pairs from negative ones
cannot be captured, and thus making them underperformed. In this paper, we
propose a novel deep multiplicative integration gating function, which answers
the question of \emph{what-and-where to match} for effective person re-id. To
address \emph{what} to match, our deep network emphasizes common local patterns
by learning joint representations in a multiplicative way. The network
comprises two Convolutional Neural Networks (CNNs) to extract convolutional
activations, and generates relevant descriptors for pedestrian matching. This
thus, leads to flexible representations for pair-wise images. To address
\emph{where} to match, we combat the spatial misalignment by performing
spatially recurrent pooling via a four-directional recurrent neural network to
impose spatial dependency over all positions with respect to the entire image.
The proposed network is designed to be end-to-end trainable to characterize
local pairwise feature interactions in a spatially aligned manner. To
demonstrate the superiority of our method, extensive experiments are conducted
over three benchmark data sets: VIPeR, CUHK03 and Market-1501.Comment: Published at Pattern Recognition, Elsevie
Practical Techniques for Improving Performance and Evaluating Security on Circuit Designs
As the modern semiconductor technology approaches to nanometer era, integrated circuits (ICs) are facing more and more challenges in meeting performance demand and security. With the expansion of markets in mobile and consumer electronics, the increasing demands require much faster delivery of reliable and secure IC products. In order to improve the performance and evaluate the security of emerging circuits, we present three practical techniques on approximate computing, split manufacturing and analog layout automation. Approximate computing is a promising approach for low-power IC design. Although a few accuracy-configurable adder (ACA) designs have been developed in the past, these designs tend to incur large area overheads as they rely on either redundant computing or complicated carry prediction. We investigate a simple ACA design that contains no redundancy or error detection/correction circuitry and uses very simple carry prediction. The simulation results show that our design dominates the latest previous work on accuracy-delay-power tradeoff while using 39% less area. One variant of this design provides finer-grained and larger tunability than that of the previous works. Moreover, we propose a delay-adaptive self-configuration technique to further improve the accuracy-delay-power tradeoff. Split manufacturing prevents attacks from an untrusted foundry. The untrusted foundry has front-end-of-line (FEOL) layout and the original circuit netlist and attempts to identify critical components on the layout for Trojan insertion. Although defense methods for this scenario have been developed, the corresponding attack technique is not well explored. Hence, the defense methods are mostly evaluated with the k-security metric without actual attacks. We develop a new attack technique based on structural pattern matching. Experimental comparison with existing attack shows that the new attack technique achieves about the same success rate with much faster speed for cases without the k-security defense, and has a much better success rate at the same runtime for cases with the k-security defense. The results offer an alternative and practical interpretation for k-security in split manufacturing.
Analog layout automation is still far behind its digital counterpart. We develop the layout automation framework for analog/mixed-signal ICs. A hierarchical layout synthesis flow which works in bottom-up manner is presented. To ensure the qualified layouts for better circuit performance, we use the constraint-driven placement and routing methodology which employs the expert knowledge via design constraints. The constraint-driven placement uses simulated annealing process to find the optimal solution. The packing represented by sequence pairs and constraint graphs can simultaneously handle different kinds of placement constraints. The constraint-driven routing consists of two stages, integer linear programming (ILP) based global routing and sequential detailed routing. The experiment results demonstrate that our flow can handle complicated hierarchical designs with multiple design constraints. Furthermore, the placement performance can be further improved by using mixed-size block placement which works on large blocks in priority
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