379 research outputs found
Maximizing CNN Accelerator Efficiency Through Resource Partitioning
Convolutional neural networks (CNNs) are revolutionizing machine learning,
but they present significant computational challenges. Recently, many
FPGA-based accelerators have been proposed to improve the performance and
efficiency of CNNs. Current approaches construct a single processor that
computes the CNN layers one at a time; the processor is optimized to maximize
the throughput at which the collection of layers is computed. However, this
approach leads to inefficient designs because the same processor structure is
used to compute CNN layers of radically varying dimensions.
We present a new CNN accelerator paradigm and an accompanying automated
design methodology that partitions the available FPGA resources into multiple
processors, each of which is tailored for a different subset of the CNN
convolutional layers. Using the same FPGA resources as a single large
processor, multiple smaller specialized processors increase computational
efficiency and lead to a higher overall throughput. Our design methodology
achieves 3.8x higher throughput than the state-of-the-art approach on
evaluating the popular AlexNet CNN on a Xilinx Virtex-7 FPGA. For the more
recent SqueezeNet and GoogLeNet, the speedups are 2.2x and 2.0x
PIMSYN: Synthesizing Processing-in-memory CNN Accelerators
Processing-in-memory architectures have been regarded as a promising solution
for CNN acceleration. Existing PIM accelerator designs rely heavily on the
experience of experts and require significant manual design overhead. Manual
design cannot effectively optimize and explore architecture implementations. In
this work, we develop an automatic framework PIMSYN for synthesizing PIM-based
CNN accelerators, which greatly facilitates architecture design and helps
generate energyefficient accelerators. PIMSYN can automatically transform CNN
applications into execution workflows and hardware construction of PIM
accelerators. To systematically optimize the architecture, we embed an
architectural exploration flow into the synthesis framework, providing a more
comprehensive design space. Experiments demonstrate that PIMSYN improves the
power efficiency by several times compared with existing works. PIMSYN can be
obtained from https://github.com/lixixi-jook/PIMSYN-NN
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