CNS: a GPU-based framework for simulating cortically-organized networks
Computational models whose organization is inspired by the cortex are increasing in both number and popularity. Current instances of such models include convolutional networks, HMAX, Hierarchical Temporal Memory, and deep belief networks. These models present two practical challenges. First, they ar...
| Main Authors: | , , |
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| Other Authors: | |
| Published: |
2010
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| Online Access: | http://hdl.handle.net/1721.1/51839 |
| _version_ | 1826198268954017792 |
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| author | Poggio, Tomaso Knoblich, Ulf Mutch, Jim |
| author2 | Tomaso Poggio |
| author_facet | Tomaso Poggio Poggio, Tomaso Knoblich, Ulf Mutch, Jim |
| author_sort | Poggio, Tomaso |
| collection | MIT |
| description | Computational models whose organization is inspired by the cortex are increasing in both number and popularity. Current instances of such models include convolutional networks, HMAX, Hierarchical Temporal Memory, and deep belief networks. These models present two practical challenges. First, they are computationally intensive. Second, while the operations performed by individual cells, or units, are typically simple, the code needed to keep track of network connectivity can quickly become complicated, leading to programs that are difficult to write and to modify. Massively parallel commodity computing hardware has recently become available in the form of general-purpose GPUs. This helps address the first problem but exacerbates the second. GPU programming adds an extra layer of difficulty, further discouraging exploration. To address these concerns, we have created a programming framework called CNS ('Cortical Network Simulator'). CNS models are automatically compiled and run on a GPU, typically 80-100x faster than on a single CPU, without the user having to learn any GPU programming. A novel scheme for the parametric specification of network connectivity allows the user to focus on writing just the code executed by a single cell. We hope that the ability to rapidly define and run cortically-inspired models will facilitate research in the cortical modeling community. CNS is available under the GNU General Public License. |
| first_indexed | 2024-09-23T11:02:08Z |
| id | mit-1721.1/51839 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T11:02:08Z |
| publishDate | 2010 |
| record_format | dspace |
| spelling | mit-1721.1/518392019-04-10T12:17:14Z CNS: a GPU-based framework for simulating cortically-organized networks Poggio, Tomaso Knoblich, Ulf Mutch, Jim Tomaso Poggio Center for Biological and Computational Learning (CBCL) Computational models whose organization is inspired by the cortex are increasing in both number and popularity. Current instances of such models include convolutional networks, HMAX, Hierarchical Temporal Memory, and deep belief networks. These models present two practical challenges. First, they are computationally intensive. Second, while the operations performed by individual cells, or units, are typically simple, the code needed to keep track of network connectivity can quickly become complicated, leading to programs that are difficult to write and to modify. Massively parallel commodity computing hardware has recently become available in the form of general-purpose GPUs. This helps address the first problem but exacerbates the second. GPU programming adds an extra layer of difficulty, further discouraging exploration. To address these concerns, we have created a programming framework called CNS ('Cortical Network Simulator'). CNS models are automatically compiled and run on a GPU, typically 80-100x faster than on a single CPU, without the user having to learn any GPU programming. A novel scheme for the parametric specification of network connectivity allows the user to focus on writing just the code executed by a single cell. We hope that the ability to rapidly define and run cortically-inspired models will facilitate research in the cortical modeling community. CNS is available under the GNU General Public License. 2010-02-26T20:30:02Z 2010-02-26T20:30:02Z 2010-02-26 http://hdl.handle.net/1721.1/51839 CBCL-286 MIT-CSAIL-TR-2010-013 11 p. application/pdf |
| spellingShingle | Poggio, Tomaso Knoblich, Ulf Mutch, Jim CNS: a GPU-based framework for simulating cortically-organized networks |
| title | CNS: a GPU-based framework for simulating cortically-organized networks |
| title_full | CNS: a GPU-based framework for simulating cortically-organized networks |
| title_fullStr | CNS: a GPU-based framework for simulating cortically-organized networks |
| title_full_unstemmed | CNS: a GPU-based framework for simulating cortically-organized networks |
| title_short | CNS: a GPU-based framework for simulating cortically-organized networks |
| title_sort | cns a gpu based framework for simulating cortically organized networks |
| url | http://hdl.handle.net/1721.1/51839 |
| work_keys_str_mv | AT poggiotomaso cnsagpubasedframeworkforsimulatingcorticallyorganizednetworks AT knoblichulf cnsagpubasedframeworkforsimulatingcorticallyorganizednetworks AT mutchjim cnsagpubasedframeworkforsimulatingcorticallyorganizednetworks |