2 research outputs found
Layer-compensated Pruning for Resource-constrained Convolutional Neural Networks
Resource-efficient convolution neural networks enable not only the
intelligence on edge devices but also opportunities in system-level
optimization such as scheduling. In this work, we aim to improve the
performance of resource-constrained filter pruning by merging two sub-problems
commonly considered, i.e., (i) how many filters to prune for each layer and
(ii) which filters to prune given a per-layer pruning budget, into a global
filter ranking problem. Our framework entails a novel algorithm, dubbed
layer-compensated pruning, where meta-learning is involved to determine better
solutions. We show empirically that the proposed algorithm is superior to prior
art in both effectiveness and efficiency. Specifically, we reduce the accuracy
gap between the pruned and original networks from 0.9% to 0.7% with 8x
reduction in time needed for meta-learning, i.e., from 1 hour down to 7
minutes. To this end, we demonstrate the effectiveness of our algorithm using
ResNet and MobileNetV2 networks under CIFAR-10, ImageNet, and Bird-200
datasets.Comment: 11 pages, 8 figures, work in progres
TIMELY: Pushing Data Movements and Interfaces in PIM Accelerators Towards Local and in Time Domain
Resistive-random-access-memory (ReRAM) based processing-in-memory (RPIM)
accelerators show promise in bridging the gap between Internet of Thing
devices' constrained resources and Convolutional/Deep Neural Networks'
(CNNs/DNNs') prohibitive energy cost. Specifically, RPIM accelerators
enhance energy efficiency by eliminating the cost of weight movements and
improving the computational density through ReRAM's high density. However, the
energy efficiency is still limited by the dominant energy cost of input and
partial sum (Psum) movements and the cost of digital-to-analog (D/A) and
analog-to-digital (A/D) interfaces. In this work, we identify three
energy-saving opportunities in RPIM accelerators: analog data locality,
time-domain interfacing, and input access reduction, and propose an innovative
RPIM accelerator called TIMELY, with three key contributions: (1) TIMELY
adopts analog local buffers (ALBs) within ReRAM crossbars to greatly enhance
the data locality, minimizing the energy overheads of both input and Psum
movements; (2) TIMELY largely reduces the energy of each single D/A (and A/D)
conversion and the total number of conversions by using time-domain interfaces
(TDIs) and the employed ALBs, respectively; (3) we develop an only-once input
read (OIR) mapping method to further decrease the energy of input accesses
and the number of D/A conversions. The evaluation with more than 10 CNN/DNN
models and various chip configurations shows that, TIMELY outperforms the
baseline RPIM accelerator, PRIME, by one order of magnitude in energy
efficiency while maintaining better computational density (up to 31.2)
and throughput (up to 736.6). Furthermore, comprehensive studies are
performed to evaluate the effectiveness of the proposed ALB, TDI, and OIR
innovations in terms of energy savings and area reduction.Comment: Accepted by 47th International Symposium on Computer Architecture
(ISCA'2020