A Comprehensive Workflow for General-Purpose Neural Modeling with Highly Configurable Neuromorphic Hardware Systems

In this paper we present a methodological framework that meets novel requirements emerging from upcoming types of accelerated and highly configurable neuromorphic hardware systems. We describe in detail a device with 45 million programmable and dynamic synapses that is currently under development, and we sketch the conceptual challenges that arise from taking this platform into operation. More specifically, we aim at the establishment of this neuromorphic system as a flexible and neuroscientifically valuable modeling tool that can be used by non-hardware-experts. We consider various functional aspects to be crucial for this purpose, and we introduce a consistent workflow with detailed descriptions of all involved modules that implement the suggested steps: The integration of the hardware interface into the simulator-independent model description language PyNN; a fully automated translation between the PyNN domain and appropriate hardware configurations; an executable specification of the future neuromorphic system that can be seamlessly integrated into this biology-to-hardware mapping process as a test bench for all software layers and possible hardware design modifications; an evaluation scheme that deploys models from a dedicated benchmark library, compares the results generated by virtual or prototype hardware devices with reference software simulations and analyzes the differences. The integration of these components into one hardware-software workflow provides an ecosystem for ongoing preparative studies that support the hardware design process and represents the basis for the maturity of the model-to-hardware mapping software. The functionality and flexibility of the latter is proven with a variety of experimental results

Optical interconnection networks based on microring resonators

Optical microring resonators can be integrated on a chip to perform switching operations directly in the optical domain. Thus they become a building block to create switching elements in on-chip optical interconnection networks, which promise to overcome some of the limitations of current electronic networks. However, the peculiar asymmetric power losses of microring resonators impose new constraints on the design and control of on-chip optical networks. In this work, we study the design of multistage interconnection networks optimized for a particular metric that we name the degradation index, which characterizes the asymmetric behavior of microrings. We also propose a routing control algorithm to maximize the overall throughput, considering the maximum allowed degradation index as a constrain

Survey of partitioning techniques in silicon compilation

In the silicon compilation design process, partitioning is usually the first problem to be investigated because partitioning algorithms form the backbone of many algorithms including: system synthesis, processor synthesis, floorplanning, and placement. In this survey, several partitioning techniques will be examined. In addition, this paper will review the partitioning algorithms used by synthesis systems at different design levels

SLAM : an automated structure to layout synthesis system

SLAM is a structure to layout synthesis system. It incorporates parameterisable bit-sliced and glue-logic generators to produce high density layout. In this paper, we describe a sliced layout architecture and SLAM system. In addition, we present partitioning algorithms for generating the floorplan for such an architecture. The algorithms partition the netlist into component sets best suited for different layout styles such as bit-sliced or strip-oriented logic. Each group is partitioned further into clusters to achieve better area utilization. Several experiments demonstrate that highly dense layouts can be achieved by using these algorithms with the sliced layout architecture

Memory and information processing in neuromorphic systems

A striking difference between brain-inspired neuromorphic processors and current von Neumann processors architectures is the way in which memory and processing is organized. As Information and Communication Technologies continue to address the need for increased computational power through the increase of cores within a digital processor, neuromorphic engineers and scientists can complement this need by building processor architectures where memory is distributed with the processing. In this paper we present a survey of brain-inspired processor architectures that support models of cortical networks and deep neural networks. These architectures range from serial clocked implementations of multi-neuron systems to massively parallel asynchronous ones and from purely digital systems to mixed analog/digital systems which implement more biological-like models of neurons and synapses together with a suite of adaptation and learning mechanisms analogous to the ones found in biological nervous systems. We describe the advantages of the different approaches being pursued and present the challenges that need to be addressed for building artificial neural processing systems that can display the richness of behaviors seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed neuromorphic computing platforms and system