Adaptive motor control and learning in a spiking neural network realised on a mixed-signal neuromorphic processor

Neuromorphic computing is a new paradigm for design of both the computing hardware and algorithms inspired by biological neural networks. The event-based nature and the inherent parallelism make neuromorphic computing a promising paradigm for building efficient neural network based architectures for control of fast and agile robots. In this paper, we present a spiking neural network architecture that uses sensory feedback to control rotational velocity of a robotic vehicle. When the velocity reaches the target value, the mapping from the target velocity of the vehicle to the correct motor command, both represented in the spiking neural network on the neuromorphic device, is autonomously stored on the device using on-chip plastic synaptic weights. We validate the controller using a wheel motor of a miniature mobile vehicle and inertia measurement unit as the sensory feedback and demonstrate online learning of a simple 'inverse model' in a two-layer spiking neural network on the neuromorphic chip. The prototype neuromorphic device that features 256 spiking neurons allows us to realise a simple proof of concept architecture for the purely neuromorphic motor control and learning. The architecture can be easily scaled-up if a larger neuromorphic device is available.Comment: 6+1 pages, 4 figures, will appear in one of the Robotics conference

Format Abstraction for Sparse Tensor Algebra Compilers

This paper shows how to build a sparse tensor algebra compiler that is agnostic to tensor formats (data layouts). We develop an interface that describes formats in terms of their capabilities and properties, and show how to build a modular code generator where new formats can be added as plugins. We then describe six implementations of the interface that compose to form the dense, CSR/CSF, COO, DIA, ELL, and HASH tensor formats and countless variants thereof. With these implementations at hand, our code generator can generate code to compute any tensor algebra expression on any combination of the aforementioned formats. To demonstrate our technique, we have implemented it in the taco tensor algebra compiler. Our modular code generator design makes it simple to add support for new tensor formats, and the performance of the generated code is competitive with hand-optimized implementations. Furthermore, by extending taco to support a wider range of formats specialized for different application and data characteristics, we can improve end-user application performance. For example, if input data is provided in the COO format, our technique allows computing a single matrix-vector multiplication directly with the data in COO, which is up to 3.6$\times$ faster than by first converting the data to CSR.Comment: Presented at OOPSLA 201

Formal Representation of the SS-DB Benchmark and Experimental Evaluation in EXTASCID

Evaluating the performance of scientific data processing systems is a difficult task considering the plethora of application-specific solutions available in this landscape and the lack of a generally-accepted benchmark. The dual structure of scientific data coupled with the complex nature of processing complicate the evaluation procedure further. SS-DB is the first attempt to define a general benchmark for complex scientific processing over raw and derived data. It fails to draw sufficient attention though because of the ambiguous plain language specification and the extraordinary SciDB results. In this paper, we remedy the shortcomings of the original SS-DB specification by providing a formal representation in terms of ArrayQL algebra operators and ArrayQL/SciQL constructs. These are the first formal representations of the SS-DB benchmark. Starting from the formal representation, we give a reference implementation and present benchmark results in EXTASCID, a novel system for scientific data processing. EXTASCID is complete in providing native support both for array and relational data and extensible in executing any user code inside the system by the means of a configurable metaoperator. These features result in an order of magnitude improvement over SciDB at data loading, extracting derived data, and operations over derived data.Comment: 32 pages, 3 figure

Compressing High-Dimensional Data Spaces Using Non-Differential Augmented Vector Quantization

query processing times and space requirements. Database compression has been discovered to alleviate the I/O bottleneck, reduce disk space, improve disk access speed, speed up query, reduce overall retrieval time and increase the effective I/O bandwidth. However, random access to individual tuples in a compressed database is very difficult to achieve with most available compression techniques. We propose a lossless compression technique called non-differential augmented vector quantization, a close variant of the novel augmented vector quantization. The technique is applicable to a collection of tuples and especially effective for tuples with many low to medium cardinality fields. In addition, the technique supports standard database operations, permits very fast random access and atomic decompression of tuples in large collections. The technique maps a database relation into a static bitmap index cached access structure. Consequently, we were able to achieve substantial savings in space by storing each database tuple as a bit value in the computer memory. Important distinguishing characteristics of our technique is that individual tuples can be compressed and decompressed, rather than a full page or entire relation at a time, (b) the information needed for tuple compression and decompression can reside in the memory or at worst in a single page. Promising application domains include decision support systems, statistical databases and life databases with low cardinality fields and possibly no text field

A Survey on Array Storage, Query Languages, and Systems

Since scientific investigation is one of the most important providers of massive amounts of ordered data, there is a renewed interest in array data processing in the context of Big Data. To the best of our knowledge, a unified resource that summarizes and analyzes array processing research over its long existence is currently missing. In this survey, we provide a guide for past, present, and future research in array processing. The survey is organized along three main topics. Array storage discusses all the aspects related to array partitioning into chunks. The identification of a reduced set of array operators to form the foundation for an array query language is analyzed across multiple such proposals. Lastly, we survey real systems for array processing. The result is a thorough survey on array data storage and processing that should be consulted by anyone interested in this research topic, independent of experience level. The survey is not complete though. We greatly appreciate pointers towards any work we might have forgotten to mention.Comment: 44 page

A Defence of Cartesian Materialism

One of the principal tasks Dennett sets himself in "Consciousness Explained" is to demolish the Cartesian theatre model of phenomenal consciousness, which in its contemporary garb takes the form of Cartesian materialism: the idea that conscious experience is a process of presentation realized in the physical materials of the brain. The now standard response to Dennett is that, in focusing on Cartesian materialism, he attacks an impossibly naive account of consciousness held by no one currently working in cognitive science or the philosophy of mind. Our response is quite different. We believe that, once properly formulated, Cartesian materialism is no straw man. Rather, it is an attractive hypothesis about the relationship between the computational architecture of the brain and phenomenal consciousness, and hence one that is worthy of further exploration. Consequently, our primary aim in this paper is to defend Cartesian materialism from Dennett's assault. We do this by showing that Dennett's argument against this position is founded on an implicit assumption (about the relationship between phenomenal experience and information coding in the brain), which while valid in the context of classical cognitive science, is not forced on connectionism

Simulation-based high-level synthesis of Nyquist-rate data converters using MATLAB/SIMULINK

This paper presents a toolbox for the simulation, optimization and high-level synthesis of Nyquist-rate Analog-to-Digital (A/D) and Digital-to-Analog (D/A) Converters in MATLAB®. The embedded simulator uses SIMULINK® C-coded S-functions to model all required subcircuits including their main error mechanisms. This approach allows to drastically speed up the simulation CPU-time up to 2 orders of magnitude as compared with previous approaches - based on the use of SIMULINK® elementary blocks. Moreover, S-functions are more suitable for implementing a more detailed description of the circuit. For all subcircuits, the accuracy of the behavioral models has been verified by electrical simulation using HSPICE. For synthesis purposes, the simulator is used for performance evaluation and combined with an hybrid optimizer for design parameter selection. The optimizer combines adaptive statistical optimization algorithm inspired in simulated annealing with a design-oriented formulation of the cost function. It has been integrated in the MATLAB/SIMULINK® platform by using the MATLAB® engine library, so that the optimization core runs in background while MATLAB® acts as a computation engine. The implementation on the MATLAB® platform brings numerous advantages in terms of signal processing, high flexibility for tool expansion and simulation with other electronic subsystems. Additionally, the presented toolbox comprises a friendly graphical user interface to allow the designer to browse through all steps of the simulation, synthesis and post-processing of results. In order to illustrate the capabilities of the toolbox, a 0.13)im CMOS 12bit@80MS/s analog front-end for broadband power line communications, made up of a pipeline ADC and a current steering DAC, is synthesized and high-level sized. Different experiments show the effectiveness of the proposed methodology.Ministerio de Ciencia y Tecnología TIC2003-02355RAICONI