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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NeuralHMC: an efficient HMC-based accelerator for deep neural networks
In Deep Neural Network (DNN) applications, energy consumption and performance cost of moving data between memory hierarchy and computational units are significantly higher than that of the computation itself. Process-in-memory (PIM) architecture such as Hybrid Memory Cube (HMC), becomes an excellent candidate to improve the data locality for efficient DNN execution. However, it’s still hard to efficiently deploy large-scale matrix computation in DNN on HMC because of its coarse grained packet protocol. In this work, we propose NeuralHMC, the first HMC-based accelerator tailored for efficient DNN execution. Experimental results show that NeuralHMC reduces the data movement by 1.4x to 2.5x (depending on the DNN data reuse strategy) compared to Von Neumann architecture. Furthermore, compared to state-of-the-art PIM-based DNN accelerator, NeuralHMC can promisingly improve the system performance by 4.1x and reduces energy by 1.5x, on average.
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- Award ID(s):
- 1725456
- PAR ID:
- 10112328
- Date Published:
- Journal Name:
- Asia and South Pacific Design Automation Conference
- Page Range / eLocation ID:
- 394 to 399
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3287624.3287642
