Stefan Schiefer

VLSI Implementation of a 2.8 Gevent/s Packet-Based AER Interface with Routing and Event Sorting Functionality

State-of-the-art large-scale neuromorphic systems require sophisticated spike event communication between units of the neural network. We present a high-speed communication infrastructure for a waferscale neuromorphic system, based on application-specific neuromorphic communication ICs in an field programmable gate arrays (FPGA)-maintained environment. The ICs implement configurable axonal delays, as required for certain types of dynamic processing or for emulating spike-based learning among distant cortical areas. Measurements are presented which show the efficacy of these delays in influencing behavior of neuromorphic benchmarks. The specialized, dedicated address-event-representation communication in most current systems requires separate, low-bandwidth configuration channels. In contrast, the configuration of the waferscale neuromorphic system is also handled by the digital packet-based pulse channel, which transmits configuration data at the full bandwidth otherwise used for pulse transmission. The overall so-called pulse communication subgroup (ICs and FPGA) delivers a factor 25–50 more event transmission rate than other current neuromorphic communication infrastructures.

Live demonstration: A scaled-down version of the BrainScaleS wafer-scale neuromorphic system

This demonstration is based on the wafer-scale neuromophic system presented in the previous papers by Schemmel et. al. (20120), Scholze et. al. (2011) and Millner et. al. (2010). The demonstration setup will allow the visitors to monitor and partially manipulate the neural events at every level. They will get an insight into the complex interplay between packet-based and realtime communication necessary to combine continuous-time mixed-signal neural networks with a packet-based transport network. Several network experiments implemented on the setup will be accessible for user interaction.

10.7 A 105GOPS 36mm 2 heterogeneous SDR MPSoC with energy-aware dynamic scheduling and iterative detection-decoding for 4G in 65nm CMOS

Modern mobile communication systems face conflicting design constraints. On the one hand, the expanding variety of transmission modes calls for highly flexible solutions supporting the ever-growing number and diversity of application requirements. On the other hand, stringent power restrictions (e.g., at femto base stations and terminals) must be considered, while satisfying the demanding performance requirements. In order to cope with these issues, existing SDR platforms, e.g. [1-2], propose an MPSoC with a heterogeneous array of processing elements (PEs). MPSoC solutions provide programmability and parallelism yielding flexibility, processing performance and power efficiency. To schedule the resources and to apply power gating, a static approach is employed. In contrast, we present a heterogeneous MPSoC platform (Tomahawk2) with runtime scheduling and fine-grained hierarchical power management. This solution can fully adapt to the dynamically varying workload and semi-deterministic behavior in modern concurrent wireless applications. The proposed dynamic scheduler (CoreManager, CM) can be implemented either in software on a general-purpose processor or on a dedicated application-specific hardware unit. It is evident that the software approach offers the highest degree of flexibility; however, it may become a performance-bottleneck for complex applications. A high-throughput ASIC was presented in [3], but this solution does not permit scheduling algorithms to be adjusted. In this work, these limitations are overcome by implementing the CM on an ASIP.

Highly integrated packet-based AER communication infrastructure with 3Gevent/S throughput

One of the main challenges in large scale neuromorphic VLSI systems is the design of the communication infrastructure. Traditionally, the neural communication has been done via parallel asynchronous transmission of Address-Event-Representations (AER) of pulses, while the configuration was achieved via off-the-shelf chip connect protocols. Recently, there has been a move towards greater event transmission speed via a serialization of the AER protocols, as well as an integration of both communication and configuration in the same interface. We present the PCB and FPGA design of such an interface for a newly developed waferscale neuromorphic system. The serial event communication of other current approaches has been refined into a packet based synchronous (rather than asynchronous) protocol, which offers better flexibility and bandwidth utilization. A factor 30–100 greater event transmission rate has been achieved. Compared to other approaches, the full communication bandwidth can also be employed for configuration. The system offers additional functionality, such as event storage and replay. Also, a very high degree of mechanical integration has been achieved.

Neuromorphic hardware in the loop: Training a deep spiking network on the BrainScaleS wafer-scale system

Emulating spiking neural networks on analog neuromorphic hardware offers several advantages over simulating them on conventional computers, particularly in terms of speed and energy consumption. However, this usually comes at the cost of reduced control over the dynamics of the emulated networks. In this paper, we demonstrate how iterative training of a hardware-emulated network can compensate for anomalies induced by the analog substrate. We first convert a deep neural network trained in software to a spiking network on the BrainScaleS wafer-scale neuromorphic system, thereby enabling an acceleration factor of 10000 compared to the biological time domain. This mapping is followed by the in-the-loop training, where in each training step, the network activity is first recorded in hardware and then used to compute the parameter updates in software via backpropagation. An essential finding is that the parameter updates do not have to be precise, but only need to approximately follow the correct gradient, which simplifies the computation of updates. Using this approach, after only several tens of iterations, the spiking network shows an accuracy close to the ideal software-emulated prototype. The presented techniques show that deep spiking networks emulated on analog neuromorphic devices can attain good computational performance despite the inherent variations of the analog substrate.

An MPSoC for energy-efficient database query processing

This paper presents a heterogeneous database hardware accelerator MPSoC manufactured in 28 nm SLP CMOS. The 18 mm2 chip integrates a runtime task scheduling unit for energy-efficient query processing and hierarchical power management supported by an ultra-fast dynamic voltage and frequency scaling. Four processing elements, connected by a star-mesh network-on-chip, are accelerated by an instruction set extension tailored to fundamental dataintensive applications. We evaluate the MPSoC with typical database benchmarks focusing on scans and bitmap operations. When the processing elements operate on data stored in local memories, the chip consumes 250 mW and shows a 96x energy efficiency improvement compared to state-of-the-art platforms.

A Heterogeneous SDR MPSoC in 28 nm CMOS for Low-Latency Wireless Applications

Current and future applications impose high demands on software-defined radio (SDR) platforms in terms of latency, reliability, and flexibility. This paper presents a heterogeneous SDR MPSoC with a hexagonal network-on-chip to address these issues. It features four data processing modules and a baseband processing engine for iterative multiple-input multiple-output (MIMO) receiving. Integrated memory controllers enable dynamic data flow mapping and application isolation. In a 4 × 4 MIMO application scenario, the MPSoC achieves a throughput of 232 Mbit/s with a latency of 20 µs while consuming 414 mW. It outperforms state-of-the-art platforms in terms of throughput by a factor of 4.

A database accelerator for energy-efficient query processing and optimization

Data processing on a continuously growing amount of information and the increasing power restrictions have become an ubiquitous challenge in our world today. Besides parallel computing, a promising approach to improve the energy efficiency of current systems is to integrate specialized hardware. This paper presents a Tensilica RISC processor extended with an instruction set to accelerate basic database operators frequently used in modern database systems. The core was taped out in a 28 nm SLP CMOS technology and allows energy-efficient query processing as well as query optimization by applying selectivity estimation techniques. Our chip measurements show an 1000x energy improvement on selected database operators compared to state-of-the-art systems.

Dynamic voltage and frequency scaling for neuromorphic many-core systems

We present a dynamic voltage and frequency scaling technique within SoCs for per-core power management: the architecture allows for individual, self triggered performance-level scaling of the processing elements (PEs) within less than 100ns. This technique enables each core to adjust its local supply voltage and frequency depending on its current computational load. A test chip has been implemented in 28nm CMOS technology, as prototype of the SpiNNaker2 neuromorphic many core system, containing 4 PEs which are operational within the range of 1.1V down to 0.7V at frequencies from 666MHz down to 100MHz; the effectiveness of the power management technique is demonstrated using a standard benchmark from the application domain. The particular domain area of this application specific processor is real-time neuromorphics. Using a standard benchmark — the synfire chain — we show that the total power consumption can be reduced by 45%, with 85% baseline power reduction and a 30% reduction of energy per neuron and synapse computation, all while maintaining biological real-time operation.

Pattern representation and recognition with accelerated analog neuromorphic systems

Despite being originally inspired by the central nervous system, artificial neural networks have diverged from their biological archetypes as they have been remodeled to fit, particular tasks. In this paper, we review several possibilites to reverse map these architectures to biologically more realistic spiking networks with the aim of emulating them on fast, low-power neuromorphic hardware. Since many of these devices employ analog components, which cannot, be perfectly controlled, finding ways to compensate for the resulting effects represents a key challenge. Here, we discuss three different, strategies to address this problem: the addition of auxiliary network components for stabilizing activity, the utilization of inherently robust, architectures and a training method for hardware-emulated networks that, functions without, perfect, knowledge of the systems dynamics and parameters. For all three scenarios, we corroborate our theoretical considerations with experimental results on accelerated analog neuromorphic platforms.