Abstract
This paper presents the VLSI implementation of a scalable and programmable Continuous Restricted Boltzmann Machine (CRBM), a probabilistic model proved useful for recognising biomedical data. Each single-chip system contains 10 stochastic neurons and 25 adaptable connections. The scalability allows the network size to be expanded by interconnecting multiple chips, and the programmability allows all parameters to be set and refreshed to optimum values. In addition, current-mode computation is employed to increase dynamic ranges of signals, and a noise generator is included to induce continous-valued stochasticity on chip. The circuit design and corresponding measurement results are described and discussed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Tong, B.T., Johannessen, E.A., Lei, W., Astaras, A., Ahmadian, M., Murray, A.F., Cooper, J.M., Beaumont, S.P., Flynn, B.W., Cumming, D.R.S.: Toward a miniature wireless integrated multisensor microsystem for industrial and biomedical applications. IEEE Sensors J. 2(6), 628–635 (2002)
Johannessen, E.A., Wang, L., Wyse, C., Cumming, D.R.S., Cooper, J.M.A.: Biocompatibility of a Lab-on-a-Pill Sensor in Artificial Gastrointestinal Environments. IEEE Transactions on Biomedical Engineering 53(11), 2333–2340 (2006)
Genov, R., Cauwenberghs, G.: Kerneltron: support vector machine in silicon. IEEE Trans. on Neural Networks 14(8), 1426–1433 (2003)
Hsu, D., Bridges, S., Figueroa, M., Diorio, C.: Adaptive Quantization and Density Estimation in Silicon. In: Advances in Neural Information Processing Systems, pp. 1083–1090. MIT Press, Cambridge (2002)
Chen, H., Murray, A.F.: Continuous restricted Boltzmann machine with an implementable training algorithm. IEE Proceedings-Vision Image and Signal Processing 150(3), 153–158 (2003)
Chen, H., Fleury, P.C.D., Murray, A.F.: Continuous-valued probabilistic behavior in a VLSI generative model. IEEE Trans. on Neural Networks 17(3), 755–770 (2006)
Chen, H., Fleury, P., Murray, A.F.: Minimizing Contrastive Divergence in Noisy, Mixed-mode VLSI Neurons. In: Advances in Neural Information Processing Systems (NIPS 2003). MIT Press, Camgridge (2004)
Lu, C.C., Hong, C.Y., Chen, H.: A Scalable and Programmable Architecture for the Continuous Restricted Boltzmann Machine in VLSI. In: IEEE International Symposium on Circuits and Systems (2007)
Cauwenberghs, G.: An Analog VLSI Recurrent Neural Network Learning a Continuous-Time Trajectory. IEEE Transactions on Neural Networks 7(2), 346–361 (2003)
Liu, S.-C., Kramer, J., Indiveri, G., Delbriick, T., Douglas, R.: Analog VLSI: Circuits and Principles. MIT Press, MA (2002)
Al-Sarawi, S.F.: A novel linear resistor utilizing MOS transistors with identical sizes and one controlling voltage. Microelectronics Journal 33(12), 1059–1069 (2002)
Cauwenberghs, G.: Delta-sigma cellular automata for analog VLSI random vector generation. IEEE Transaction on Circuit and System II: Analog and Digital Signal Processing 46(3), 240–250 (1999)
Diotalevi, F., Valle, M., Bo, G.M., Biglieri, E., Caviglia, D.D.: Analog CMOS current mode neural primitives. Circuit and System II: Analog and Digital Signal Processing. In: IEEE International Symposium on Circuits and Systems (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lu, CC., Chen, H. (2009). Current-Mode Computation with Noise in a Scalable and Programmable Probabilistic Neural VLSI System. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5768. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04274-4_42
Download citation
DOI: https://doi.org/10.1007/978-3-642-04274-4_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04273-7
Online ISBN: 978-3-642-04274-4
eBook Packages: Computer ScienceComputer Science (R0)