More Like this

1. Stochastic-HD: Leveraging Stochastic Computing on the Hyper-Dimensional Computing Pipeline

   https://doi.org/10.3389/fnins.2022.867192  

   Morris, Justin; Hao, Yilun; Gupta, Saransh; Khaleghi, Behnam; Aksanli, Baris; Rosing, Tajana (May 2022, Frontiers in Neuroscience)

   Brain-inspired Hyper-dimensional(HD) computing is a novel and efficient computing paradigm. However, highly parallel architectures such as Processing-in-Memory(PIM) are bottle-necked by reduction operations required such as accumulation. To reduce this bottle-neck of HD computing in PIM, we present Stochastic-HD that combines the simplicity of operations in Stochastic Computing (SC) with the complex task solving capabilities of the latest HD computing algorithms. Stochastic-HD leverages deterministic SC, which enables all of HD operations to be done as highly parallel bitwise operations and removes all reduction operations, thus improving the throughput of PIM. To this end, we propose an in-memory hardware design for Stochastic-HD that exploits its high level of parallelism and robustness to approximation. Our hardware uses in-memory bitwise operations along with associative memory-like operations to enable a fast and energy-efficient implementation. With Stochastic-HD, we were able to reach a comparable accuracy with the Baseline-HD. Furthermore, by proposing an integrated Stochastic-HD retraining approach Stochastic-HD is able to reduce the accuracy loss to just 0.3%. We additionally accelerate the retraining process in our hardware design to create an end-to-end accelerator for Stochastic-HD. Finally, we also add support for HD Clustering to Stochastic-HD, which is the first to map the HD Clustering operations to the stochastic domain. As compared to the best PIM design for HD, Stochastic-HD is also 4.4% more accurate and 43.1× more energy-efficient. 

   more » « less
2. Printed Stochastic Computing Neural Networks

   https://doi.org/10.23919/DATE51398.2021.9474254  

   Weller, Dennis; Bleier, Nathaniel; Hefenbrock, Michael; Aghassi-Hagmann, Jasmin; Beigl, Michael; Kumar, Rakesh; Tahoori, Mehdi (January 2021, Design and Test in Europe (DATE))

   null (Ed.)
3. Sound Source Localization Using Stochastic Computing

   https://doi.org/10.1145/3508352.3549373  

   Schober, Peter; Estiri, Seyedeh Newsha; Aygun, Sercan; TaheriNejad, Nima; Najafi, M. Hassan (October 2022, 41st IEEE/ACM International Conference on Computer-Aided Design)

   Stochastic computing (SC) is an alternative computing paradigm that possesses data in the form of long uniform bit-streams rather than conventional compact weighted binary numbers. SC is fault-tolerant and can compute on small, efficient circuits, promising advantages over conventional arithmetic for smaller computer chips. SC has been primarily used in scientific research, not in practical applications. Digital sound source localization (SSL) is a useful signal processing technique that locates speakers using multiple microphones in cell phones, laptops, and other voice-controlled devices. SC has not been integrated into SSL in practice or theory. In this work, for the first time to the best of our knowledge, we implement an SSL algorithm in the stochastic domain and develop a functional SC-based sound source localizer. The developed design can replace the conventional design of the algorithm. The practical part of this work shows that the proposed stochastic circuit does not rely on conventional analog-to-digital conversion and can process data in the form of pulss-width-mudulated (PWM) signals. 

   more » « less
4. FUNSC: A GUI Software for Stochastic Computing

   https://doi.org/10.1109/HORA61326.2024.10550497  

   Huse, Jackson P; Salehi, Sayed A (May 2024, IEEE)
5. COSMO: Computing with Stochastic Numbers in Memory

   https://doi.org/10.1145/3484731  

   Gupta, Saransh; Imani, Mohsen; Sim, Joonseop; Huang, Andrew; Wu, Fan; Kang, Jaeyoung; Kim, Yeseong; Rosing, Tajana Šimunić (April 2022, ACM Journal on Emerging Technologies in Computing Systems)

   Stochastic computing (SC) reduces the complexity of computation by representing numbers with long streams of independent bits. However, increasing performance in SC comes with either an increase in area or a loss in accuracy. Processing in memory (PIM) computes data in-place while having high memory density and supporting bit-parallel operations with low energy consumption. In this article, we propose COSMO, an architecture for co mputing with s tochastic numbers in me mo ry, which enables SC in memory. The proposed architecture is general and can be used for a wide range of applications. It is a highly dense and parallel architecture that supports most SC encodings and operations in memory. It maximizes the performance and energy efficiency of SC by introducing several innovations: (i) in-memory parallel stochastic number generation, (ii) efficient implication-based logic in memory, (iii) novel memory bit line segmenting, (iv) a new memory-compatible SC addition operation, and (v) enabling flexible block allocation. To show the generality and efficiency of our stochastic architecture, we implement image processing, deep neural networks (DNNs), and hyperdimensional (HD) computing on the proposed hardware. Our evaluations show that running DNN inference on COSMO is 141× faster and 80× more energy efficient as compared to GPU. 

   more » « less