A Framework for Modeling, Optimizing, and Implementing DNNs on FPGA Using HLS

Shahshahani, Masoud; Khabbazan, Bahareh; Sabri, Mohammad; Bhatia, Dinesh

doi:10.1109/dcas51144.2020.9330667

Cited by 4 publications

(1 citation statement)

References 12 publications

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“…Embedded Devices: We compare the power efficiency of COSMO with the state-of-the-art implementations of DNN inference on NVIDIA Tegra Jetson X1 GPU [65], FPGA [78], and Edge-TPU [3]. For the inference task on AlexNet, Tegra X1 (FPGA) achieves a power efficiency of 45 images/s/W (16 images/s/W), while COSMO achieves 506 images/s/W.…”

Section: Cosmo Vs Previous Acceleratorsmentioning

confidence: 99%

COSMO: Computing with Stochastic Numbers in Memory

Gupta

Imani

Sim

et al. 2022

J. Emerg. Technol. Comput. Syst.

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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.

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Section: Cosmo Vs Previous Acceleratorsmentioning

confidence: 99%