13 research outputs found
Active Learning for Deep Neural Networks on Edge Devices
When dealing with deep neural network (DNN) applications on edge devices,
continuously updating the model is important. Although updating a model with
real incoming data is ideal, using all of them is not always feasible due to
limits, such as labeling and communication costs. Thus, it is necessary to
filter and select the data to use for training (i.e., active learning) on the
device. In this paper, we formalize a practical active learning problem for
DNNs on edge devices and propose a general task-agnostic framework to tackle
this problem, which reduces it to a stream submodular maximization. This
framework is light enough to be run with low computational resources, yet
provides solutions whose quality is theoretically guaranteed thanks to the
submodular property. Through this framework, we can configure data selection
criteria flexibly, including using methods proposed in previous active learning
studies. We evaluate our approach on both classification and object detection
tasks in a practical setting to simulate a real-life scenario. The results of
our study show that the proposed framework outperforms all other methods in
both tasks, while running at a practical speed on real devices
Fast and Space-Efficient Parallel Algorithms for Influence Maximization
Influence Maximization (IM) is a crucial problem in data science. The goal is
to find a fixed-size set of highly-influential seed vertices on a network to
maximize the influence spread along the edges. While IM is NP-hard on
commonly-used diffusion models, a greedy algorithm can achieve
-approximation, repeatedly selecting the vertex with the highest
marginal gain in influence as the seed. Due to theoretical guarantees, rich
literature focuses on improving the performance of the greedy algorithm. To
estimate the marginal gain, existing work either runs Monte Carlo (MC)
simulations of influence spread or pre-stores hundreds of sketches (usually
per-vertex information). However, these approaches can be inefficient in time
(MC simulation) or space (storing sketches), preventing the ideas from scaling
to today's large-scale graphs.
This paper significantly improves the scalability of IM using two key
techniques. The first is a sketch-compression technique for the independent
cascading model on undirected graphs. It allows combining the simulation and
sketching approaches to achieve a time-space tradeoff. The second technique
includes new data structures for parallel seed selection. Using our new
approaches, we implemented PaC-IM: Parallel and Compressed IM.
We compare PaC-IM with state-of-the-art parallel IM systems on a 96-core
machine with 1.5TB memory. PaC-IM can process large-scale graphs with up to
900M vertices and 74B edges in about 2 hours. On average across all tested
graphs, our uncompressed version is 5--18 faster and about 1.4
more space-efficient than existing parallel IM systems. Using compression
further saves 3.8 space with only 70% overhead in time on average
Machine learning applied to accelerate energy consumption models in computing simulators
The ever-increasing growth of data centres and fog resources makes difficult for current simulation frameworks to model large computing infrastructures. Therefore, a major trade-off for simulators is the balance between abstraction level of the models, the scalability, and the performance of the executions. In order to balance better these, early forays can be found in the literature in which AI techniques are applied, but either lack of generality or are tailored to specific simulation frameworks. This paper describes the methodology to integrate memoization as a technique of supervised learning into any computing simulators framework. In this process, a bespoke kernel was constructed for the analysis of the energy models used in most well known computing simulators -cloud and fog-, but also to avoid simulation overhead. Finally, a detailed evaluation of energy models and its performance is presented showing the impact of applying supervised learning to computing simulator, showing performance improvements when models are more accurate and computations are dense
LIPIcs, Volume 274, ESA 2023, Complete Volume
LIPIcs, Volume 274, ESA 2023, Complete Volum
Novel Computational Methods for Integrated Circuit Reverse Engineering
Production of Integrated Circuits (ICs) has been largely strengthened by globalization. System-on-chip providers are capable of utilizing many different providers which can be responsible for a single task. This horizontal structure drastically improves to time-to-market and reduces manufacturing cost. However, untrust of oversea foundries threatens to dismantle the complex economic model currently in place. Many Intellectual Property (IP) consumers become concerned over what potentially malicious or unspecified logic might reside within their application. This logic which is inserted with the intention of causing harm to a consumer has been referred to as a Hardware Trojan (HT). To help IP consumers, researchers have looked into methods for finding HTs. Such methods tend to rely on high-level information relating to the circuit, which might not be accessible. There is a high possibility that IP is delivered in the gate or layout level. Some services and image processing methods can be leveraged to convert layout level information to gate-level, but such formats are incompatible with detection schemes that require hardware description language. By leveraging standard graph and dynamic programming algorithms a set of tools is developed that can help bridge the gap between gate-level netlist access and HT detection. To help in this endeavor this dissertation focuses on several problems associated with reverse engineering ICs. Logic signal identification is used to find malicious signals, and logic desynthesis is used to extract high level details. Each of the proposed method have their results analyzed for accuracy and runtime. It is found that method for finding logic tends to be the most difficult task, in part due to the degree of heuristic\u27s inaccuracy. With minor improvements moderate sized ICs could have their high-level function recovered within minutes, which would allow for a trained eye or automated methods to more easily detect discrepancies within a circuit\u27s design