We are building a family of AI Inference Engines for edge applications, combining unprecedented performance with very low power / low energy operation and a lower cost of ownership.
We have an extensive patent portfolio on RRAM and related that will be disclosed shortly.
- X. Tang, G. Kim, P.-E. Gaillardon and G. De Micheli, "A Study on the Programming Structures for RRAM-Based FPGA Architectures," in IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 63, no. 4, pp. 503-516, April 2016. doi: 10.1109/TCSI.2016.2528079
- X. Tang, P.-E. Gaillardon and G. De Micheli, "FPGA-SPICE: A Simulation-based Power Estimation Framework for FPGAs," 2015 33rd IEEE International Conference on Computer Design (ICCD), New York, NY, 2015, pp. 696-703. doi: 10.1109/ICCD.2015.7357183
- X. Tang, E. Giacomin, G. De Micheli and P.-E. Gaillardon, "Post-P&R Performance and Power Analysis for RRAM-Based FPGAs," in IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 8, no. 3, pp. 639-650, Sept. 2018. doi: 10.1109/JETCAS.2018.2847600
- X. Tang, E. Giacomin, G. De Micheli and P.-E. Gaillardon, "Circuit Designs of High-Performance and Low-Power RRAM-Based Multiplexers Based on 4T(ransistor)1R(RAM) Programming Structure," in IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 64, no. 5, pp. 1173-1186, May 2017. doi: 10.1109/TCSI.2016.2638542