Implementation and Characterization of Mixed-Signal Neuromorphic ASICs

Accelerated neuromorphic hardware allows the emulation of spiking neural networks with a high speed-up factor compared to classical computer simulation approaches. However, realizing a high degree of versatility and configurability in the implemented models is challenging. In this thesis, we present two mixed-signal ASICs that improve upon previous architectures by augmenting the versatility of the modeled synapses and neurons. In the first part, we present the integration of an analog multi-compartment neuron model into the Multi-Compartment Chip. We characterize the properties of this neuron model and describe methods to compensate for deviations from ideal behavior introduced by the physical implementation. The implemented features of the multi-compartment neurons are demonstrated with a compact prototype setup. In the second part, the integration of a general-purpose microprocessor with analog models of neurons and synapses is described. This allows to define learning rules that go beyond spike-timing dependent plasticity in software without decreasing the speed-up of the underlying network emulation. In the third part, the importance of testability and pre-tapeout verification is discussed and exemplified by the design process of both chips

SOFIR: Securely Outsourced Forensic Image Recognition

Forensic image recognition tools are used by law enforcement agencies all over the world to automatically detect illegal images on confiscated equipment. This detection is commonly done with the help of a strictly confidential database consisting of hash values of known illegal images. To detect and mitigate the distribution of illegal images, for instance in network traffic of companies or Internet service providers, it is desirable to outsource the recognition of illegal images to these companies. However, law enforcement agencies want to keep their hash databases secret at all costs as an unwanted release may result in misuse which could ultimately render these databases useless.\ud We present SOFIR, a tool for the Secure Outsourcing of Forensic Image Recognition allowing companies and law enforcement agencies to jointly detect illegal network traffic at its source, thus facilitating immediate regulatory actions. SOFIR cryptographically hides the hash database from the involved companies. At fixed intervals, SOFIR sends out an encrypted report to the law enforcement agency that only contains the number of found illegal images in the given interval, while otherwise keeping the company’s legal network traffic private. Our experimental results show the effectiveness and practicality of our approach in the real-world

Artificial Intelligence based Position Detection for Hydraulic Cylinders using Scattering Parameters

Position detection of hydraulic cylinder pistons is crucial for numerous industrial automation applications. A typical traditional method is to excite electromagnetic waves in the cylinder structure and analytically solve the piston position based on the scattering parameters measured by a sensor. The core of this approach is a physical model that mathematically describes the relationship between the measured scattering parameters and the targeted piston position. However, this physical model has shortcomings in accuracy and adaptability, especially in extreme conditions. To overcome this problem, we propose Artificial Intelligence (AI)-based methods to learn the relationship directly data-driven. As a result, all Artificial Neural Network (ANN) models in this paper consistently outperform the physical one by a large margin. Given the success of AI-based models for our task, we further deliberate the choice of models based on domain knowledge and provide in-depth analyses combining model performance with the physical characteristics. Specifically, we use Convolutional Neural Network (CNN)s to discover local interactions of input among adjacent frequencies, apply Complex-Valued Neural Network (CVNN) to exploit the complex-valued nature of electromagnetic scattering parameters, and introduce a novel technique named Frequency Encoding to add weighted frequency information to the model input. By combining these three techniques, our best performing model, a complex-valued CNN with Frequency Encoding, manages to significantly reduce the test error to hardly 1/12 of the one given by the traditional physical model.Comment: 16 pages, 10 figure

Novel African trypanocidal agents: membrane rigidifying peptides

The bloodstream developmental forms of pathogenic African trypanosomes are uniquely susceptible to killing by small hydrophobic peptides. Trypanocidal activity is conferred by peptide hydrophobicity and charge distribution and results from increased rigidity of the plasma membrane. Structural analysis of lipid-associated peptide suggests a mechanism of phospholipid clamping in which an internal hydrophobic bulge anchors the peptide in the membrane and positively charged moieties at the termini coordinate phosphates of the polar lipid headgroups. This mechanism reveals a necessary phenotype in bloodstream form African trypanosomes, high membrane fluidity, and we suggest that targeting the plasma membrane lipid bilayer as a whole may be a novel strategy for the development of new pharmaceutical agents. Additionally, the peptides we have described may be valuable tools for probing the biosynthetic machinery responsible for the unique composition and characteristics of African trypanosome plasma membranes

Demonstrating Advantages of Neuromorphic Computation: A Pilot Study

Neuromorphic devices represent an attempt to mimic aspects of the brain's architecture and dynamics with the aim of replicating its hallmark functional capabilities in terms of computational power, robust learning and energy efficiency. We employ a single-chip prototype of the BrainScaleS 2 neuromorphic system to implement a proof-of-concept demonstration of reward-modulated spike-timing-dependent plasticity in a spiking network that learns to play the Pong video game by smooth pursuit. This system combines an electronic mixed-signal substrate for emulating neuron and synapse dynamics with an embedded digital processor for on-chip learning, which in this work also serves to simulate the virtual environment and learning agent. The analog emulation of neuronal membrane dynamics enables a 1000-fold acceleration with respect to biological real-time, with the entire chip operating on a power budget of 57mW. Compared to an equivalent simulation using state-of-the-art software, the on-chip emulation is at least one order of magnitude faster and three orders of magnitude more energy-efficient. We demonstrate how on-chip learning can mitigate the effects of fixed-pattern noise, which is unavoidable in analog substrates, while making use of temporal variability for action exploration. Learning compensates imperfections of the physical substrate, as manifested in neuronal parameter variability, by adapting synaptic weights to match respective excitability of individual neurons.Comment: Added measurements with noise in NEST simulation, add notice about journal publication. Frontiers in Neuromorphic Engineering (2019

Accelerated physical emulation of Bayesian inference in spiking neural networks

The massively parallel nature of biological information processing plays an important role for its superiority to human-engineered computing devices. In particular, it may hold the key to overcoming the von Neumann bottleneck that limits contemporary computer architectures. Physical-model neuromorphic devices seek to replicate not only this inherent parallelism, but also aspects of its microscopic dynamics in analog circuits emulating neurons and synapses. However, these machines require network models that are not only adept at solving particular tasks, but that can also cope with the inherent imperfections of analog substrates. We present a spiking network model that performs Bayesian inference through sampling on the BrainScaleS neuromorphic platform, where we use it for generative and discriminative computations on visual data. By illustrating its functionality on this platform, we implicitly demonstrate its robustness to various substrate-specific distortive effects, as well as its accelerated capability for computation. These results showcase the advantages of brain-inspired physical computation and provide important building blocks for large-scale neuromorphic applications.Comment: This preprint has been published 2019 November 14. Please cite as: Kungl A. F. et al. (2019) Accelerated Physical Emulation of Bayesian Inference in Spiking Neural Networks. Front. Neurosci. 13:1201. doi: 10.3389/fnins.2019.0120

Distributed Searchable Symmetric Encryption

Searchable Symmetric Encryption (SSE) allows a client to store encrypted data on a storage provider in such a way, that the client is able to search and retrieve the data selectively without the storage provider learning the contents of the data or the words being searched for. Practical SSE schemes usually leak (sensitive) information during or after a query (e.g., the search pattern). Secure schemes on the other hand are not practical, namely they are neither efficient in the computational search complexity, nor scalable with large data sets. To achieve efficiency and security at the same time, we introduce the concept of distributed SSE (DSSE), which uses a query proxy in addition to the storage provider.\ud We give a construction that combines an inverted index approach (for efficiency) with scrambling functions used in private information retrieval (PIR) (for security). The proposed scheme, which is entirely based on XOR operations and pseudo-random functions, is efficient and does not leak the search pattern. For instance, a secure search in an index over one million documents and 500 keywords is executed in less than 1 second

The Wooster Voice (Wooster, OH), 1997-10-16

This edition of the College of Wooster student run newspaper was published on October 16 of 1997 and it is twelve pages long. Jilted surgeon general calls for more health education, Dr. Jocelyn Elders presents during the Wooster Forum series about health care and education. Ebert to be dedicated this weekend, the Ebert Art Center is going to be formally dedicated. Renowned musical trio combines classical with new, the Cleveland Duo and saxophonist James Umble perform at the Gault Recital Hall. Athletic updates for the past week are highlighted on pages ten to twelve.https://openworks.wooster.edu/voice1991-2000/1177/thumbnail.jp