665 research outputs found
NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors
© 2016 Cheung, Schultz and Luk.NeuroFlow is a scalable spiking neural network simulation platform for off-the-shelf high performance computing systems using customizable hardware processors such as Field-Programmable Gate Arrays (FPGAs). Unlike multi-core processors and application-specific integrated circuits, the processor architecture of NeuroFlow can be redesigned and reconfigured to suit a particular simulation to deliver optimized performance, such as the degree of parallelism to employ. The compilation process supports using PyNN, a simulator-independent neural network description language, to configure the processor. NeuroFlow supports a number of commonly used current or conductance based neuronal models such as integrate-and-fire and Izhikevich models, and the spike-timing-dependent plasticity (STDP) rule for learning. A 6-FPGA system can simulate a network of up to ~600,000 neurons and can achieve a real-time performance of 400,000 neurons. Using one FPGA, NeuroFlow delivers a speedup of up to 33.6 times the speed of an 8-core processor, or 2.83 times the speed of GPU-based platforms. With high flexibility and throughput, NeuroFlow provides a viable environment for large-scale neural network simulation
A RECONFIGURABLE AND EXTENSIBLE EXPLORATION PLATFORM FOR FUTURE HETEROGENEOUS SYSTEMS
Accelerator-based -or heterogeneous- computing has become increasingly
important in a variety of scenarios, ranging from High-Performance Computing (HPC) to embedded systems. While most solutions use sometimes
custom-made components, most of today’s systems rely on commodity highend CPUs and/or GPU devices, which deliver adequate performance while
ensuring programmability, productivity, and application portability. Unfortunately, pure general-purpose hardware is affected by inherently limited
power-efficiency, that is, low GFLOPS-per-Watt, now considered as a primary metric. The many-core model and architectural customization can
play here a key role, as they enable unprecedented levels of power-efficiency
compared to CPUs/GPUs. However, such paradigms are still immature and
deeper exploration is indispensable.
This dissertation investigates customizability and proposes novel solutions
for heterogeneous architectures, focusing on mechanisms related to coherence and network-on-chip (NoC). First, the work presents a non-coherent
scratchpad memory with a configurable bank remapping system to reduce
bank conflicts. The experimental results show the benefits of both using a
customizable hardware bank remapping function and non-coherent memories for some types of algorithms. Next, we demonstrate how a distributed
synchronization master better suits many-cores than standard centralized
solutions. This solution, inspired by the directory-based coherence mechanism, supports concurrent synchronizations without relying on memory
transactions. The results collected for different NoC sizes provided indications about the area overheads incurred by our solution and demonstrated
the benefits of using a dedicated hardware synchronization support. Finally, this dissertation proposes an advanced coherence subsystem, based
on the sparse directory approach, with a selective coherence maintenance
system which allows coherence to be deactivated for blocks that do not require it. Experimental results show that the use of a hybrid coherent and
non-coherent architectural mechanism along with an extended coherence
protocol can enhance performance.
The above results were all collected by means of a modular and customizable heterogeneous many-core system developed to support the exploration
of power-efficient high-performance computing architectures. The system is
based on a NoC and a customizable GPU-like accelerator core, as well as
a reconfigurable coherence subsystem, ensuring application-specific configuration capabilities. All the explored solutions were evaluated on this real heterogeneous system, which comes along with the above methodological
results as part of the contribution in this dissertation. In fact, as a key
benefit, the experimental platform enables users to integrate novel hardware/software solutions on a full-system scale, whereas existing platforms
do not always support a comprehensive heterogeneous architecture exploration
NaNet:a low-latency NIC enabling GPU-based, real-time low level trigger systems
We implemented the NaNet FPGA-based PCI2 Gen2 GbE/APElink NIC, featuring
GPUDirect RDMA capabilities and UDP protocol management offloading. NaNet is
able to receive a UDP input data stream from its GbE interface and redirect it,
without any intermediate buffering or CPU intervention, to the memory of a
Fermi/Kepler GPU hosted on the same PCIe bus, provided that the two devices
share the same upstream root complex. Synthetic benchmarks for latency and
bandwidth are presented. We describe how NaNet can be employed in the prototype
of the GPU-based RICH low-level trigger processor of the NA62 CERN experiment,
to implement the data link between the TEL62 readout boards and the low level
trigger processor. Results for the throughput and latency of the integrated
system are presented and discussed.Comment: Proceedings for the 20th International Conference on Computing in
High Energy and Nuclear Physics (CHEP
- …