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Archive for August, 2010

Emulation Engine for Spiking Neurons and Adaptive Synaptic Weights

August 31st, 2010 1 comment

PCNN (Pulse-Coded Neural Networks) : A modeled network system which is for the evaluation of a biology-oriented image processing, usually performed on general-purpose computers, e. g. PCs or workstations.
SNNs(Spiking Neural Networks) : A neural network model. In addition to neuronal and synaptic state, SNNs also incorporate the concept of time into their operating model.

SEE(Spiking Neural Network Emulation Engine) : A field-programmable gate array(FPAG) based emulation engine for spiking neurons and adaptive synaptic weights is presented, that tackles bottle-neck problem by providing a distributed memory architecture and a high bandwidth to the weight memory.

PCNN – Operated by PC & workstation -> Time consuming
– Because of bottle-neck : sequential access weight memory

FPGA SEE – Distribute memory
– High bandwidth weight memory
– separating calculation neuron states & network topology

SNNs or PCNNs 1. Reproduce spike or pulse.
2. Perform some problems such as vision tasks.

Problem of Large PCNNs 1. Calculation steps.
2. Communication resources.
3. Load balancing.
4. Storage capacity.
5. Memory bandwidth.

Spiking neuron model with adaptive synapses.

Non-leaky integrate-and-fire neuron(IFN)

,    ,  

Overview of the SEE architecture

Overview of SEE architecture

A. Simulation control(PPC2) 1. Configuration of network

2. Monitoring of network parameter.

3. Administration of event-list.

– Two event-lists  :  DEL(Dynamic Event-List) includes all excited neurons that receive

a spike or an external input current.

FEL(Fire Event-List) stores all firing neurons that are in a spike

sending state and the corresponding time values when the

neuron enters the spike receiving state again.

B. Network Topology Computation(NTC)

– Topology-vector-phase : The presynaptic activity is determined for each excited neuron.

– Topology-update-phase : The tag-fields are updated according to occurred spike start-events or spike stop-events.

C. Neuron State Computation(NSC)

– Neuron-spike-phase : It is determined if before the next spike stop-event an excited

neuron will start to fire.

– Neuron –update-phase

– Bulirsch_Stoer method of integration.(MMID, PZEXTR)

– Modified-midpoint integration(MMID)

– Polynomial extrapolation(PZEXTR)

PCB of spiking neural network emulation engine

Performance analysis

n NNEURON NBSSTEP TSW TSEE FSPEED-UP
4 32X32 98717 1405 s 45 s 31.2
48X48 222365 6527 s 226 s 28.9
64X64 420299 22620 s 758 s 29.8
80X80 721463 65277 s 2032 s 32.1
96X96 926458 119109 s 3757 s 31.7
8 32X32 107276 1990 s 63 s 31.6
48X48 235863 7263 s 312 s 23.3
64X64 413861 31548 s 972 s 32.5
80X80 645694 80378 s 2370 s 33.9
96X96 967572 142834 s 5113 s 29.9

– Reference

Emulation Engine for Spiking Neurons and Adaptive Synaptic Weights by H. H. Hellmich, M. Geike, P. Griep, P. Mahr, M. Rafanelli and H. Klar.

Categories: Emerging Topics, Review Tags: ,

Circular Stack Management for Scratchpad Memory

August 20th, 2010 No comments
A nice Stack Management technique for a limited local memory architecture like IBM Cell, is presented at ASP-DAC and in IEEE Trans. CAD in 2009. The pointer problem was extremely difficult to handle correctly, but we managed to do it, at least from the correctness point of view.

A dynamic scratch pad memory (SPM) management scheme for program stack data with the objective of processor power  reduction is presented.  Basic  technique  does  not  need the SPM size at compile time, does not mandate any hardware changes, does not need profile information, and seamlessly integrates support for recursive functions. Stack frames are managed using a software SPM manager, integrated into the application binary, and shows average energy savings of 32% along with a performance improvement of 13%, on benchmarks from MiBench. SPM management  can  be further optimized and made pointer-safe, by knowing the SPM size.

Read the full paper: “A Software-Only Solution to Use Scratch Pads for Stack Data,” by Aviral Shrivastava, Arun Kannan, and Jongeun Lee*, published in IEEE Transactions on CAD, vol. 28, no. 11, pp. 1719-1727, November 2009.

Categories: Multicore, Publications Tags: , , , ,

Have you figured out how this WordPress works?

August 20th, 2010 No comments

Honestly i’m a little confused. So I’m trying things out. My questions:

  • What is Category. Is it really different from tag?
  • Is there category view, which is different from menu view.
  • Is there any way to post static html page directly (not through a link)
Categories: Uncategorized Tags:

WordPress Installed!

August 19th, 2010 Comments off

Done it at last.

Contributors: Youngmoon Um (right) and Mooyoung Lee (left), both are majoring in ECE at UNIST.

Categories: Uncategorized Tags:

Jongeun Lee

August 10th, 2010 No comments

Jongeun Lee

Jongeun Lee joined Ulsan National Institute of Science and Technology (UNIST) in August of 2009 and is Associate Professor of Electrical and Computer Engineering. Dr. Lee received his B.S. (1997) and M.S. (1999) in Electrical Engineering, and his Ph.D. (2004) in Electrical Engineering and Computer Science all from Seoul National University.

Prior to joining UNIST, Dr. Lee was a Senior Researcher at Samsung Electronics SoC Research Center (2004.1-2007.10) and a Postdoctoral Research Associate at the Arizona State University (2007.11-2009.8). Dr. Lee is a recipient of Postdoctoral Research Fellowship from KRF (Korea Research Foundation) in 2007.

Dr. Lee has published more than 70 papers in refereed journals and conferences as well as a book chapter and several patents. His research interest includes deep learning processors, reconfigurable processor, and compilation for low power, reliability, and multi-core processors.

Categories: People Tags: