CREW: Computation reuse and efficient weight storage for hardware-accelerated MLPs and RNNs
Deep Neural Networks (DNNs) have achieved tremendous success for cognitive applications. The core operation in a DNN is the dot product between quantized inputs and weights. Prior works exploit the weight/input repetition that arises due to quantization to avoid redundant computations in Convolution...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/374069 |
| Acceso en línea: | https://hdl.handle.net/2117/374069 https://dx.doi.org/10.1016/j.sysarc.2022.102604 |
| Access Level: | acceso abierto |
| Palabra clave: | Machine learning Neural networks (Computer science) Deep neural networks Computation reuse Accelerators Low energy Aprenentatge automàtic Xarxes neuronals (Informàtica) Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors |
| Sumario: | Deep Neural Networks (DNNs) have achieved tremendous success for cognitive applications. The core operation in a DNN is the dot product between quantized inputs and weights. Prior works exploit the weight/input repetition that arises due to quantization to avoid redundant computations in Convolutional Neural Networks (CNNs). However, in this paper we show that their effectiveness is severely limited when applied to FullyConnected (FC) layers, which are commonly used in state-of-the-art DNNs, as it is the case of modern Recurrent Neural Networks (RNNs) and Transformer models. To improve energy-efficiency of FC computation we present CREW, a hardware accelerator that implements Computation Reuse and an Efficient Weight Storage mechanism to exploit the large number of repeated weights in FC layers. CREW first performs the multiplications of the unique weights by their respective inputs and stores the results in an on-chip buffer. The storage requirements are modest due to the small number of unique weights and the relatively small size of the input compared to convolutional layers. Next, CREW computes each output by fetching and adding its required products. To this end, each weight is replaced offline by an index in the buffer of unique products. Indices are typically smaller than the quantized weights, since the number of unique weights for each input tends to be much lower than the range of quantized weights, which reduces storage and memory bandwidth requirements. Overall, CREW greatly reduces the number of multiplications and provides significant savings in model memory footprint and memory bandwidth usage. We evaluate CREW on a diverse set of modern DNNs. On average, CREW provides 2.61x speedup and 2.42x energy savings over a TPU-like accelerator. Compared to UCNN, a state-of-art computation reuse technique, CREW achieves 2.10x speedup and 2.08x energy savings on average. |
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