CNN Compression and Search Using Set Transformations with Width Modifiers on Network Architectures
Authors: Bilal Siddiqui
Dakai Zhu
Venue: Submitted to ICLR 2023 (2023)
Keywords: cnn
compression
efficient search
sets
embedded systems

Abstract: We propose a new approach, based on discrete filter pruning, to adapt off-the-shelf models into an embedded
environment. Importantly, we circumvent the usually prohibitive costs of model compression. Our method, Structured
Coarse Block Pruning (SCBP), prunes whole CNN kernels using width modifiers applied to a novel transformation of
convlayers into superblocks. SCBP uses set representations to construct a rudimentary search to provide candidate
networks. To test our approach, the original ResNet architectures serve as the baseline and also provide the 'seeds'
for our candidate search. The search produces a configurable number of compressed (derived) models. These derived models
are often ~20\% faster and ~50\% smaller than their unmodified counterparts. At the expense of accuracy, the size can
become even smaller and the inference latency lowered even further. The unique SCBP transformations yield many new model
variants, each with their own trade-offs, and does not require GPU clusters or expert humans for training and design.

CNN COMPRESSION AND SEARCH USING SET TRANS- FORMATIONS WITH WIDTH MODIFIERS ON NETWORK ARCHITECTURES
Anonymous authors Paper under double-blind review
ABSTRACT
We propose new approach, based on discrete filter pruning. to adapt models into an embedded environment. Importantly, we circumvent the usually prohibitive costs of model compression. Our method, Structured Coarse Block Pruning (SCBP), prunes whole CNN kernels using width modifiers applied to a novel transformation of convlayers into superblocks. SCBP uses set representations to construct rudimentary search to provide candidate networks. To test our approach, the original ResNet architectures serve as the baseline and also provide the 'seeds' for candidate search. The search produces configurable number of compressed (derived) models. These derived models are often 20% faster and 50% smaller than their unmodified counterparts. At the expense of accuracy, the size can become even smaller and the inference latency lowered even further. The unique SCBP transformations yield many new model variants, each with their own trade-offs, and does not require GPU clusters or expert humans for training or design.
INTRODUCTION
Modern Computer Vision (CV) dominated by the convolution operation introduced by Fukushima & Miyake (1982) and later advanced into Convolutional Neural Network (CNN convnet) by LoCun et al. (1989). Until recently, these convnets were limited to rudimentary CV tasks such as classifying handwritten digits LeCun et al. (1998). Present-day convnets have far surpassed other CV approaches by improving their framework to include faster activations Nair & Hinton (2010), stacked convolutional layers (convlayers) Krizhevsky et (2012), and better optimizers Kingma Ba (a((24)))) These multi-layer deep convnets require big data in the form of datasets such as ImageNet Deng al. (2099) to enable deep learning LeCun et al. (2015) of the feature space.
However effective, convnets are held back by their high resource consumption. Utilizing an effective convnet on the edge presents new challenges latency, energy, and memory costs Chen & Ran (2019). Additionally, many tasks, such as autonomous robotics, require realtime processing and cannot be offloaded to the cloud, As such. resource constrained platforms, such as embedded systems, lack the compute and memory to use convnets in their default constructions.
Analysis into convnets reveals that they are overparameterized Denil et al. (2013) and that reducing this overparameterization can be a key mechanism in compressing convnets Hanson & Pratt (1988)
LeCun et al.) [1990]; (Han et al. (2015a). The many weights that form a network are not necessarily of the same entropy and can therefore be seen as scaffolding to be removed during compression step Hassibi & Stork 1993); Han et al. 20155): Tessier et al. (2021).
In this work, our objective is to reduce the size of any given convnet using automated approach requiring little human engineering and compute resources. To that end, we design Structured Coarse Block Pruning (SCBP), a compressing mechanism that requires no iterative retraining or fine-tuning SCBP uses a low-cost search method, seeded with an off-the-shelf network, to generate compressed models derivatives with unique accuracy, size, and latency trade-offs.

---

The reminder of this paper is organized as follows. Section 2 focuses on closely related works. Section 3 details the methodology and implementation of SCBP. Section 4 discusses experimental findings, and finally conclude with key takeaways and future directions in Section 5.
2 RELATED WORKS
Early work on removing parameters from Artificial Neural Networks (ANNs) was focused in gaining insights on the purpose of those parameters Hanson & Pratt (988)). Prior AlexNet Krizhevsky
et al. (2012). exploiting ANN overparameterization was used as a regularization mechanism LeCun
exploited to reduce the Tessier et al. (202T). Removing parameters compresses the memory footprint CV models, which can then allow their deployment on embedded
al. 1990 Hassibi & Stork (1993). Recently, ANN overparametteriztion
size of models Han et al. (2015b); Zhou et al. Tessier
systems. Compression additionally facilitates reduced energy costs while also reducing latency by greatly reducing memory traffic Han et al. (2015a); Zhou et al. (2017).
Model accuracy is sustained or reduced, depending on the method of compression. Preserving compressed CV model's baseline accuracy is challenging and requires large compute Han al.) (2015b); Zhou et al. (017). A common mechanism for maintaining a trained model's accuracy is to iteratively reduce its size in prune-retrain cycles. Another mechanism is leveraging a Network Automated Search (NAS), often using reinforcement learning, to build networks from scratch that are both small and accurate Zoph et al, (2018); ; Cai et al. (2020). However, both prune-retrain and NAS exorbitant in compute usage, typically the order of 10 and 10 GPU hours respectively.
When computing resources are limited, faster mechanisms for compression are needed. A range of techniques are available, such tensor factorization Kim al.. (2015); |hhan et al. (2020);
Swaminathan et al.] (2020) and Fast Fourier Transforms (FFT) on CV models' weight tensors.
Hashing can also be used to group similar weights into buckets Chen al. (2015); Hu al. (2018).
These techniques, while faster to train, do not maintain the original network's 's accuracy and often produce larger models relative to prune-retrain and NAS approaches.
Quantization is also frequently used Gong et al. (014)): Wu et al. 2016) to reduce the bit-width of parameters from 64-bit floats down-to 5-bit ints or less Wu al. (2016); Zhou et al. (2017). ) In
special cases, only 2-bit parameters are sufficient Rastegari et al. (016); Courbariaux et al. (2016) 2015). Other techniques include those based on weight-decay and entropy Luo & Wu (2017 (u et al. 2018); Tessier et al. (202T).
Min
In our proposed mechanism, we bridge a gap between manual and NAS approaches by using a low-cost search to order network width attributes of any given CV model, which is partitioned by novel algorithm into multiple segments with each being assigned its own width modifier. close work is from Howard et al. (2017) in the form of MobileNets which are family of networks using different uniform width modifiers on a manually engineered baseline model. Similarly, EfficientNets Tan & Le (2019) expands the idea of modifiers to include depth and input resolution modifiers. Our approach benefits from generalized compression that can be applied to any model because we do not require new baseline that needs to be engineered and thus can keep cost within 10 GPU hours.
3 METHOD
The compression approach detailed below is realized by novel combination of convlayer binning, width modification, and unique rearch-train step based on set transforms of the aforementioned combination. Unlike most network architecture search methods that impose prohibitively long search and train times, our work circumvents the cost problem by providing halfway point between NAS and human engineered architectures. In doing so, we present a rudimentary proof of concept which, in our evaluations, can produce an efficient search and thus generate derivative models when configured by simple human defined search domain set constraints. The SCBP version we use stands four foundations: (1) a seed architecture from which derivative architectures will be produced; (2) a network segmentation mechanism for the seed architecture for binning and assist in derivation; (3) set of compression ratios (c-ratios) for each segment of the seed network; and (4) a one shot search for network instantiation based on (1)-(3)
2

---

3.1 SEED ARCHITECTURES
The instantiation of compressed, derivative architectures is sourced from a seed architecture. In this work, we use the ResNet family as seed architectures. SCBP initialized with a seed network helps down on search times by leveraging already known and working architectures to generate new derived variants. Currently, these variants do require training to convergence to determine their accuracy but their latency, memory footprint, and power stats are immediately known. As an aside, the residual connection paradigm ResNets is widely used today as the foundation of a variety of architectures. As such, using the ResNet testbed here allows for potential extension to later developed networks.
To further accelerate search, the seed architecture needs to be segmented into three portions, each of which undergoes its own unique compression. ResNet on CIFAR data consists of three superblocks where we define superblock to be all ResNet blocks of the same filter dimensions. Thus, we use three segments because our seed architecture accommodates it with little engineering. The input layer and its subsequent hown-sampling layers plus the output layers are left untouched.
Once the seed network is divided into well-defined segments, each segment is selected for width modification by applying c-ratios. Different segment and c-ratio pairings show changed weight distributions and residual functions and hence result in new derived networks. Interestingly, the features learned per superblock, to a certain extent, can change to maintain accuracy when adjacent superblocks compressed. i.e. Over-parameterized segments may absorb entropy that may be lost from adjacent c-ratios.
To detail, in each derived network, ordered tuple of c-ratios is required, where each element in the tuple corresponds to a segment and hence encodes the compression factor for that segment. Thus, of c-ratios can be constructed using the Cartesian product of R and the S of partitioned segments. Both S and R must be small countable sets to prevent combinatorial explosion. In section 4 we find excellent results with ISI 51=3 and IR| = If seed architecture can potentially benefit from different S and R, these hyperparameters can be easily changed.
The emergent property from the architecture derivation process above an effective and quick representation component for enumerating architectures that circumvents intractable search costs. The simplicity of the method allows its application to the growing library of modern convnets. It is therefore possible for many of these off-the-shelf convnets to be automatically modified via compression to meet embedded system resource constraints. This provides low-cost approach to leverage past and present architecture engineering effort in embedded use cases.
3.2 SELECTING AND APPLYING C-RATIOS TO NETWORK SEGMENTS
A segment is a binning of sequentially stacked convolutional layers of the same dimensions. These segments are craved up from the seed network. The number of segments in seed network is dependent on two factors: the seed's 's architecture and the segmenting procedure. For ResNet like models, segmenting
straight-forward: use each superblock as its own segment. different architectures, such as VGG-19, segmenting can done based on like dimension convolutional layers. The procedure should be adapted based on the seed architecture, the cardinality of R, and available compute. In algorithm I we provide generalized segmenting procedure for arbitrary convnets.
Convlayers can be represented in the ordered set C and is from here that S is constructed. Both and C are in the same set family. A one-to-ons correspondence between S and C is to be avoided, which would mean each convlayer is its own segment. A small segment count is crucial to an efficient search because it limits the number of generated derived networks, described in equation 4 We can understand each segment SES as a coarse representation of multiple CEC. In practice, algorithm implements the following superblock segment definition:
S = {s (ct,..., ,,), cu) E C,, |s| E N, k < |C]
where each is tuple of multiple The cardinality of S is not required be identical to C.
After network segmentation into superblocks, we need to pair these segments with a c-ratio. The c-ratio, at its core, is multiplicative factor applied to the filter dimension of convlayers. It is number strictly & 1.00. All baseline convolutional layers are at a default c-ratio of 1.00, and this is a valid configuration for SCBP. The c-ratio is constructed from the set R as follows:
3

---

R fr:reR and 0.0 r & 1.00)
(2)
where values <1.0 compress convlayers by discrete filter pruning (truncation), ==1.0 preserves the original convlayer width attributes, and c-ratios >1.00 are undefined in terms of compression. The operation of applying c-ratio segment essentially transform T such that:
T(m n x f k) = m x n (f r) x
(3)
Here m x n represent the filter's spacial dimensions, and is the filter count. f is modified by r, and k denotes the segment depth (simply, its the stacked convlayer count).
Selecting the correct c-ratios can both be done using search, such as grid-search, or manually. Our experiments in section use four manually selected c-ratios. In our experience, SCBP shows resiliency and works well without fine-tuned c-ratios. With that said, low-cost fine-tuning step for c-ratios a possible future direction. In this work's seed networks, the four c-ratios were hand-picked as equidistant points from one another to provide coarse, uniform coverage of the c-ratio search space. They are the set R (0.25, 0.50, 0.75, 1.00).
The segment and c-ratio sets used in conjunction to produce compressed derived networks. Derived networks can be thought of as subnets of their overparameterized seed networks. Each subnet behaves uniquely, due in part their differing weight distribution. The construction of the collection of derived networks requires both sets R and S. Each tuple rER is crossed with a seed segment sES using the Cartesian product between the sets:
[S| I R. X S (1a71,,..,,,,1,,, w.a,where R, S S, and |S|} 1-1
(4)
Each segment has its own c-ratio where the c-ratio set transformed into multiset with multiplicity equal to the segment set cardinality. The cross product between the c-ratio multiset and segment set yield the space of derived networks. As such, each derived network configured with unique memory footprints and weight tensors; these configurations can then allow similarly accurate networks to be profiled and culled for the best hardware fit.
There are no prune-retrain cycles and hyperparamcters are simply adopted from the seed network. The derived network tensors are not sparse and this non-sparsity is of great benefit; it allows hardware acceleration of multiply-ade on virtually all platforms without the need of ASICs.
3.3 APPLYING SCBP TO RESNETS AND OTHER ARCHITECTURES
CV models typically utilize stacked convlayers, where many successive layers are identical dimensions. Residual connections also common in these modern convnets. While innumerable improvements have been made activations, bottleneck layers, etc, the underlying data structures of most contemporary models stands on the foundations of convlayers and residual connections.
Algorithm [ forms the basis of SCBP as segments the seed network into superblocks by binning like-dimension convlayers. The bins sorted according to parameter count where the largest bins are selected as segments and k configurable hyperparameter (the segment set cardinality).
Algorithm 1: segmenting procedure
Algorithm 2: derivation procedure
seed select convnet(model. constraints);
segments <- segmenting procedure(:
layers + extract extract_dimssseeeesetett dimss(scd));
cratios - multiset traeeersttoooooctttiioo multiplicity
buckets - bin lae_likk_eea bittedddilllacsss,.
-
Eegments.Icngttnnn
b_uuketts - sort www..cccnnnign/uucettt)))
+ cartesian critesian_pooduursssgmetttt
segments buckets(O...k):
cratios);
return segments;
return derived segments:
To create derived segments from a binned seed network, we must first identify a set of c-ratios. In this work, we default set, given in algorithm 2 for two reasons. One, additional search is incurred,
4

---

and second, the c-ratios and their magnitude evenly covers the subspace of widths not larger than the baseline width. We exploit the block structure of our seed architecture by coalescing blocks into superblocks and then selectively compressing these superblocks such that non-uniform compression can be performed to derive both networks and insights into the original seed. Additionally, limiting c-ratios superblocks (instead of convlayers) significantly cuts down on the enumeration cost for the architecture search. The final derived segments are product of both the binned convlayers and c-ratios. This product is the building unit of each derived network.
The network generation for derived networks starts at the seed network and ends with a pool of derived networks, given in 3 The procedure acquires a seed, creates segments from the seed and then pairs the segments with c-ratios to construct derived segments. The derived segments are then pieced back together to form the new derived networks.
Algorithm 3: network generation
Algorithm train, test, rank
seed - select convnet(modell constraints); segments sggmenting_proccdurge
derived networks +- network generation(;
models e list(;
derived_ derived_segments- -
performance listO:
for convnet in derived derived_networkd networks do
derived neteworss_tworrss-ee-eo <- list(:
model - trannccoonne::
for segment tuple in derived perived__sgmmets yegments do
metrics + test(model);;
template deepcoppy:
modelk.appennd/model)
network
+ replace__ayeeee (tmpsell,
cerfoomancce.ppeedddee (mttrccssm
segment_tuple),
end
end
candidate Sociiddttemmdells +- rank(models,,
return derived_neet :
performance):
The train-test regiment remains the same the seed, but instead of one network, many more networks are trained in parallel. Each derived model has its metries logged for later ranking so that the best one be determined based user-specific constraints. In this work, c-ratio and segmenting procedures construct 64 unique models per seed. We random initialize each model, train-validate it, and finally collect metries on the testset.
SCBP is generalizable to most deep convnet architectures that use sequentially stacked convlayers. However, when the segmenting procedure unable to bin layers, is the case when all convlayers are of different dimensions, SCBP can become limited, especially when segments cover a minor subset of all the parameters. In practice, modern convnets typically use many stacked convolutional layers and thus are receptive to SCBP.
In sum, SCBP can generate compressed models without the need to modify training regiments, continuous fine-tuning, prune-retrain cycles, or even modified byperparameters. The compression of superblocks to different c-ratios can provide valuable insights into their relative importance to the rest of the network. These insights can guide efforts to build better architectures and may also help find hardware quirks that adapt better to certain derived architectures.
EVALUATIONS AND DISCUSSIONS
4.1 SETUP, METRICS, AND EXPERIMENTS
The CIFAR dataset is used to determine the effectiveness of SCBP on the compressed models. CIFAR provides a complex data distribution with coarse labels that are appropriate for approximating tasks on the edge. The dataset consists of 50K train images and 10K test images. In total, these 60K images, of dimensions 32x32x3, cover a variety of categories such as dogs and trucks. The dataset has two label sets over the same data, one with 10 coarse labels and another with 100 finer labels. We bench with on both using the top-1 metric.
Inference statistics are measured the JETSON Xavier NX and desktop WorkStation. The platforms serve to approximate real-world hardware constraints so that the effects of compression can be evaluated in terms of latency and power. In table the WorkStation is the least constraint while low memory bottlenecks while the Xavier NX provides a typical embedded environment for benching.
5

---

sblock1 sblock2
sblock3
Platform
5.196%
18.935%
75.455%
XavierNX
WorkStation
Component
Table 1: Parameter distribution. The den- sity of weights in convnets is usually spread nonuniformly. For example, our ResNet testbed has almost parameters are concentrated into high
1.1 Cache
1MB
512KB
Cache
6MB
2MB
L3 Cache
4MB
16MB
Total Cache
11MB
18.5MiB
CPU Memory
8GB
64GB
the last two superblocks. Compression density superblocks yields faster and smaller models while identical c-ratios lower density superblocks are correlated with worse accuracy.
Video Memory
8GB
11GB
Total Memory
8GB
75GB
CPU Cores
6
8
CUDA Cores
384
4352
hyperparameters
decay
epoch
momentum
learning rate Ic-1
Table 3: Hardware. The platforms above each have significantly differing computation capaci- ties which help demonstrate the effects of com- pression rates on different resource constraints. Specifically, both the embedded JETSON and Raspberry Pi use shared memory architectures for their gpu and cpu cores. The WorkStation uses dedicated graphics memory. All the plat- forms have different cache associativities, clocks speeds, and memory latencies.
1c-4
lc2
9c-1
Table 2: Hyperparameters. Every derived net- work repurposes the seed network's hyperpa- rameters. Here, the training regiment is adapted from (e et al., (2016) with no modifications to demonstrate the applicability of SCBP. Training and testing is done using PyTorch 1.9v with two RTX 2080Ti GPUs.
Performance is composed of several metrics. In experiments, settle four measures of performances: accuracy, latency, size, and power. These metrics help determine a model's 's holistic fit into an embedded environment. Top-1 accuracy is measured using the whole held-out testset. The model size is tied to superblock c-ratios and table provides a reference. Latency and energy -interall are dynamic metrics that need averaging over many readings to reduce their margin of error. Both the model latency and power draw are processed as arithmetic means from 10k inference frames.
The seed architectures are unadulterated models pulled from He al. (016)). We use ResNet20, ResNet44, and ResNetl 10 to provide 'source' candidates for the SCEP based search. Additionally, using ResNets helps to indirectly approximate their innumerable successor networks, many of which are minor architectural changes and most of which are prevalent in production today. For CIFAR 100 labels (new models), the final denselayer of the ResNets are modified to simply increase node count from 10 to 100. All models use zero-pades zero-padded summation for their residual connections.
4.2 EFFECT OF SCBP ON ACCURACY AND MODEL SIZE
This section provides overview of the empirical effects of SCBP and further analyzes its effects model architecture and performance. In particular, SCBP reveals interesting architecture patterns that discuss and highlight.
Parameter updates during training change based on the label granularity. That is, the superblock-to- accuracy relation begins shift with just change in the number of labels. When controlling for parameter count, coarse labels on shallow networks usually outperform deeper networks. Conversely, deeper networks outperform shallow networks on finer labels, even when parameter counts are similar. Label granularity additionally influences the distribution of redundant parameters, where coarse labels primarily increase redundancy in the final superblock and fine labels distribute low entropy parameters across all superblocks see table 5 Interestingly, c-ratios have similar accuracy effects, regardless of superblock filter density. For example, a c-ratio of 0.25 on the first superblock usually harms accuracy much more than the same c-ratio the significantly denser last superblock. These patterns are evident from experiments, especially with derived nets seeded with ResNet44, see table 4 The tables also indicate the learning ability of different portions within the seed networks by evaluating their response to SCBP.
In terms of accuracy resilience to truncations, both the learning task and cumulative layers up to given superblock need to be considered. The compression impact on accuracy is better correlated with layer count and data granularity than the actual parameter count. For example, the finer-grained labels

---

top-1 Awcc when 4b3-cr=0.25
top-1 Aacc when sb3-crw0.5
dhr100
2.0
1.5
15
18
80
RELATIVE
1.00
1.00
SUPERBLOCE
0,7g
0.5
1000
100
SUPERBLOCK
2.5
SUPERBLOCK
0.50
025
(a)
(h)
Figure 1: These plots demonstrate the effect of c-ratios 10.25.0.50.0.75.1.. 1000) on superblocks one and two when superblock three is fixed to (0.25,0.50). The 7-axis is the delta deviation in accuracy of the derived networks from their respective seed basclines. Like most convnets, parameters are concentrated the tail end and hence aggressive compression, such with a c-ratio <=0.5, of interest, Note that more compression has the effect of fewer derived models meeting the accuracy threshold.
CIFARI0
sblock1
sblock2
sblock3
CIFARI00
sblock1
sblock2
sblock3
cr 0.25
cr 0.25
cr 0,50
23
23
cr + 0.50
13
cr - 0.75
30
32
31
cr 0.75
13
15
14
1.00
32
39
36
1.00
12
21
26
Table 4: CIFAR10 trends. The table shows the number of derived models (out of 192) that meet an accuracy delta of +2% from the seed's baseline accuracy. Each table cell encodes the given superblock locked to the given c-ratio and all other superblocks are free variables. In conjunction with table [] we know superblock one only has 5% of pa- rameters but it disproportionally effect candidate net count with c-ratio=0.25, Sim- ilar insights guide nets with superblock three at c-ratio=0.25 and accumulate similar number of candidates with size reduction of 19% instead of just 5%. Of the 192 derived networks, 103 meet the accuracy constraint.
Table 5: CIFARI00 trends. Unlike table 4 the more 10X more labels in the dataset makes learning difficult as seen in only 49 derived networks meeting the +2% accuracy constraint, 52.4% reduction from the Cl- FAR10 result. Interestingly, the pattern of learning per superblock seems to change as reflected in the table columns. Unlike previ- ously, superblock
stays stable regardless of c-ratio, and now the other blocks suffer a 50% drop in candidate model counts for each successive c-ratio step. In short, it may be possible significantly increase compression by reducing label granularity in preprocess- ing. However, the generality and feasibility of this approach needs to be developed.
instead use derived
in CIFAR100 make compression more challenging because each filter encodes more information and therefore incurs a larger cost when is pruned. This can inferred from table and figure where the simple CIFAR10 10 labels allow SCBP to produce 110% more derived networks than with CIFAR100's finer labels.
Within the derived networks, note several learning patterns. One such pattern is that the final superblock, the largest in our experiments, counter-intuitively contributes the least to final model accuracy. In fact, even removing 50% to 75% of its filters incurs marginal accuracy effects. The large truncation allowance on the final superblock beneficial because it means that many derived models be compressed 40-50%.
Another pattern SCBP exposes is a weak co-dependence between the first two superblocks where many models lose >2% accuracy if 75% of their parameters truncated in either superblock.
7

---

top-1 Aacc when ab2-cr=0.25
top-1 Aacc when sb2-cr=0.5
dtr100
2.0
1.5
15
18
80
RELATIVE
1.00
1.00
SUPERBLOCE
0,7g
0.5
1000
100
SUPERBLOCK1
250
SUPERBLOCK
0.50
(h)
Figure 2: Here, instead superblock three, we fix superblock two's c-ratio into (0.25,0.50) and leave the other superblocks free within (0.25.0.50.0.75,1.000) Unlike the previous [ there many more derived nets that meet the accuracy threshold. Superblock two has significantly less parameters, about 19% of the total. See table for parameter distributions. The overall network compression is modest here, even with aggressive c-ratios. The noteworthy trend is significantly more derived networks meeting the accuracy threshold with many or above the seed network's baseline.
Extensive data tables are provided in Appendix 5 The co-dependence may be due to the low parameter redundancy present in the first two superblocks. Indeed, these possibly higher entropy shallow superblocks may indicate that overparameterization is mainly problem of deeper layers. The relative compression-accurag trade-off between superblocks means its better to focus on compressing latter superblocks. Additionally, it should be noted from table and figures ?? that middle superblock has large facility to absorb critical weights from its neighbors when they are highly compressed. More generally, high compression tends to cause adjacent superblocks to compensate for missing filters. After a certain amount, typically 30% size reduction, lowering model size correlates positively with lowering accuracy, albeit with some derived net exceptions that are more accurate after compression (see 10-NNer44-1.0-.5555
etc).
Our takeaway is that SCBP applied convnets effectively compress models with very low search costs. The c-ratios on superblocks help illuminate the relative contribution of parameters to final performance and therefore provide insights into the seed and possibly assist designing more compressible seed networks. And while SCBP size reductions are moderate when compared unstructured pruning, SCBP does result in weight matrix sparsity and thus benefits from BLAS- based hardware acceleration. Lastly, SCBP saves time by not requiring hyperparameter tuning or modification to the seed's training regiment. These attributes coupled with no prune-retrain cycles, elevate SCBP far above many comparable techniques in terms of GPU costs. Typically, these costs are in the range of 103 hours for pruning and up-to 10 hours for NAS. comparison, SCBP consumes less than 72 GPU hours from start to finish.
4.3 EFFECT OF SCBP ON LATENCY AND ENERGY
Inference time is end-to-end, meaning encompasses image preprocessing, data moves, model execution, and postprocessing. It is this time that is averaged for the latency and its during this duration that power draw also measured. For power draw, the Xavier NX polls INA3321 micro-controller while the WorkStation samples from turbostat and mvidia-smi.
The main determining factor for latency and power metrics model size. No matter the architecture or hardware, frequent cache misses and, worse, memory swaps absolutely crush real-time performance and balloon latency. The speed and capacity of the memory hierarchy is the determining factor of latency and hence the energy-interval. Because the main contributor latency and energy costs memory access, designing smaller convnets is an effective approach for resource constrained platforms which heavily benefit from compression due to their limited memory hierarchy.
8

---

(ote and glrt sire udgg2s
Sant and gid 122g5.t
Jus gur wtens wwggg.
Deee and girt aggan
(a)
(b)
(d)
1.3
1.-
0.1
Rutt ard
6.3
6.3
-0.
-03
-0.3
-1.
-1.0
-1.
-1.5
-15
-15
-1.
-10
-30
-34
(c)
(1)
(g)
(h)
Figure 3: XavierNX General Trends in Latency & Power The x-axis is percentage change, centered at O, where less is better (to the right). The y-axis shows the delta in accuracy from each seed ResNet's s respective baseline (positive and higher is better). The stellar pattern to note is that of latency in subfigs (a)-(d) where we see greatly reduced latencies, usually by over 20% while accuracy is largely maintained. Power draw, as seen in (e)-(h) remains steady with little delta deviation from the baseline.
In figure ! we see that derived nets can maintain their accuracy while compressed. This means there are smaller models, with possibly fewer cache misses, which could be faster. Then we see in figures (a)-(d), that indeed latency is greatly reduced, by around 20%, and 30% in some cases. Mcanwhile, the actual power draw seen figures (e)-(h) does not show significant changes meaning that overall energy-interval are reduced for our derived networks.
Lastly, we find that once a memory bottleneck is successfully eliminated, further compression comes with diminishing returns as can be observed in the WorkStation experiments in the Appendix, figure The data indicates that excessive parameter pruning, past memory bottlenecks is an unneeded computation waste. The pruning, deployed in many unstructured compression techniques does not lead to faster, less energy hungry models; often they only result in smaller models coming at the cost of very expensive and long train cycles.
5 CONCLUSION AND FUTURE WORK
We demonstrate novel mechanism to segment convlayers into superblocks and set them with different compression ratios using a set represented network search. The SCBP framework constructs pool of models with different attributes which help with different hardware fits. These models are much smaller and faster than their unmodified counterparts. The training cost of these models is low and feasible with regular workstations. As such, the embedded native models can be designed without prohibitive costs, allowing rapid iteration to find the best model-hardware pairing. For future work, are extending SCBP into an iterative mode that operates on pre-trained networks.
REFERENCES
H. Cai, C. Gan, and S. Han. Once for all: Train one network and specialize it for efficient deployment. arXiv preprint arXiv: arXiv:1908.0m 1908.0979,, 2020.
Jiasi Chen and Xukan Ran. Deep learning with edge computing: review, Proceedings of the IEEE, 1056)):65551177, 2019.
Wenlin Chen, James Wilson, Stephen Tyree, Kilian Weinberger, and Yixin Chen. Compressing neural networks with the hashing trick. In International conference on machine learning. pp. 2285-2294. PMLR, 2015.

---

Matthieu Courbariaux, Yoshua Bengio, and Jean-Pierte David. Binaryconnect: Training deep neural networks with binary weights during propagations. arXiv preprint arXiv: 1r1iv:151.00333,, 2015.
Matthieu Courbariaux, Itay Hubara, Daniel Soudry, Ran El- Vaniv, and Yoshua Bengio. Binarized neural networks: Training deep neural networks with weights and activations constrained to+ or-1. arXiv preprint (eti:v/600.288200 2016.
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scal scale hierarchical image database. In 2009 JEEE conference on computer vision and pattern recognition, pp. 248-255 leee, 2009.
Misha Denil, Babak Shakibi, Laurent Dinh, Marc' Aurelio Ranzato, and Nando De Freitas. Predicting parameters in deep learning. arXiv preprint arXiv:1306.0543 2013.
Kunihiko Fukushima and Sei Miyake. Neocognitron: self-organizing neural network model for mechanism of visual pattern recognition. In Competition and cooperation in neural nets, pp. 267-285. Springer, 1982.
Yunchao Gong, Liu Liu, Ming Yang, and Lubomir Bourdev. Compressing deep convolutional networks using vector quantization. arXiv preprint arXiv:1412.6115, 2014.
Song Han, Huizi Mao, and William J Dally. Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv preprint 1510.0: 49, 2015a.
Song Han, Jeff Pool, John Tran, and William J Dally. Learning both weights and connections for efficient neural networks. arXiv preprint arXiv: 150::550.0000 02626, 2015b.
Stephen Hanson and Lorien Pratt. Comparing biases for minimal network construction with back- propagation. Advances in neural information processing systems, 1:177-185, 1988.
Babak Hassibi and David G Stork. Second order derivatives for network pruning: Optimal brain surgeon. 1993.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference computer vision and pattern recognition, pp. 770-778, 2016.
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv: /704004861 2017.
Qinghao Hu, Peisong Wang, and Jian Cheng. From hashing cnns: Training binary weight networks via hashing. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 32, 2018.
Yong-Deok Kim, Eunhyeok Park, Sungjoo Yoo, Taelim Choi, Lu Yang. and Dongjun Shin. Com- pression of deep convolutional neural networks for fast and low power mobile applications. arXiv preprint
2015.
D.P. Kingma and Ba. Adam: A method for stochastic optimization, arXiv preprint arXiv: 2014.
A. Krizhevsky, I. Sutskever, and G.E. Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, 4(2):1097-1105,, 7 2012.
Y. LeCun, L. Bottou, Y. Bengio, and Haffner. Gradient- based learning applied to document recognition. Proceedings of the IEEE, 86:2278-2324, 11 1998.
Yann LeCun, Bernhard Boser, John S Denker, Donnie Henderson, Richard E Howard, Wayne Hubbard, and Lawrence D Jackel. Backpropagation applied to handwritten zip code recognition. Neural computation, 1(4):541-551. 1989.
Yann LeCun, John S Denker, and Sara Solla. Optimal brain damage. In Advances neural information processing systems, pp. 598-605, 1990.
10

---

Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. Deep learning. nature, 521(7553):436 444, 2015.
Jian-Hao Luo and Jianxin Wu, An entropy-based pruning method for cnn compression. arXiv preprint arXiv:1706.05790 2017.
Chuhan Min, Aosen Wang. Yiran Chen, Wenyao Xu, and Xin Chen. 2pfpce: Two-phase filter pruning based on conditional entropy. arXiv preprint arXiv:/809.0222) 2018.
Vinod Nair and Geoffrey E Hinton. Rectified linear units improve restricted boltzmann machines. In Icml, 2010.
Anh-Huy Phan, Konstantin Sobolev, Konstantin Sozykin, Dmitry Ermilov, Julia Gusak, Petr Tichavský, Valeriy Glukhov, Ivan Oseledets, and Andrzej Cichocki. Stable low-rank tensor decomposition for compression of convolutional neural network. In European Conference on Computer Vision, pp. 522-539. Springer, 2020.
Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Farhadi. Xnor-net: Imagenet classification using binary convolutional neural networks. In European conference computer vision, pp. 525-542. Springer, 2016.
Sridhar Swaminathan, Deepak Garg, Rajkumar Kannan, and Frederic Andres. Sparse low rank factorization for deep neural network compression. Neurocomputing. 398:185-1999 185-196, 2020.
Mingxing Tan and Quoe Le. Efficientnet: Rethinking model scaling for convolutional neural networks. In International Conference on Machine Learning, pp. 6105-61 14. PMLR, 2019.
Hugo Tessier, Vincent Gripon, Mathieu Léonardon, Matthieu Arzel, Thomas Hannagan, and David Bertrand. Rethinking weight decay for efficient neural network pruning. 2021.
Jiaxiang Wu, Cong Leng, Yuhang Wang, Qinghao Hu, and Jian Cheng. Quantized convolutional neural networks for mobile devices. In Proceedings of the IEEE Conference Computer Vision and Pattern Recognition, pp. 1820-4828, 2016.
Aojun Zhou, Anbang Yao, Yiwen Guo, Lin Xu, and Yurong Chen. Incremental network quantization: Towards lossless cnns with low-precision weights. arXiv preprint arXiv:1702.03000 2017.
Zoph, Vasudevan, Shlens, and Q. Le. Learning transferable architectures for scalable image recognition. In Proceedings of the IEEE conference computer vision and pattern recognition, (2) (2):6677887 7 2018

---

APPENDIX
the x-axis and WORKSTATION BENCHMARKS
Unlike figure 3 neither power nor lateney show significant deviation from the seed network's baseline, hinting at the diminishing return of pruning once bottlenecks are no longer present.
dbscc and slat 1608p3.23
Bast and Abd waees 132g8.09
baot when 182
Be:: and sk3g0.3
20
-39
(a)
(b)
(d)
1.
1.-
0.3
03-
6.3
0.0
-.5
-65
-05
-05
-1.
-10
-1.
-1.
3.5
-44
-14
-20
(c)
(f)
(g)
(h)
Figure 4: WorkStation General Trends in Latency & Power Similar to figure
y-axis are measure percentage changes.
ACCURACY AND MEMORY TABLES
The complete testing results for the derived networks are given in the tables below. Each table data cell represents single derived model's metrics with the following representation: òacc..- The accuracy (acc) of each model is the delta from its seed network. The size (sz) is given as a percentage of the seed's size; i.e. 0.6 means 60% of the seed's size or a 40% size reduction for that particular derived convnet.
The row and column heading cells denote the superblock c-ratios. Here the c-ratios are given in the form (0.25, 0.50, 0.75, 1.00) sbyt where 0.75I02 evaluates to superblock two compressed with a c-ratio of 0.75. Each table row iterates over the c-ratio set, from 0.25 1.00, modulus The columns are layered into two levels; at the top is superblock three, e.g. 0.50a53 and this only changes every four columns. Below the superblock three heading superblock two configuration, and iterates similar to the rows, ie. each column is difference c-ratio superblock two.
12

---

0.25
0.50
0.75
1.00
0.50
0.73%
0.78
1.00%
0.25
0.50"
0.75*
1.00
0,25
0.50
0.75%
1.00
0.25
-0,00.09
-7.00,13
-4.97
40
5.55
4.00
26
-3.12,64
-2.12,68
-4.51
-3.14,48
-1.09.84
03
3.233
-2.00 78
10
0
-8.22.10
-6.03.1
-4.30.2
-3433
4.74
-3.73
-2.19
-1.84.4
-..84.44
-2.00.4
-1.36.56
-0.78
-1.77
-0,98
-0.33,
0.75
-.94.12
-0.25.16
-3.9
2..7
3.59
-2.22
-1.2,
-1.88.4
-2.77
-1.92,51
-1.18.77
2.06
1.25
0.66
0..23,07
1.00
-5.98.14
-4.37,18
-3.02.24
-1.34.99
3.39
-2.17
-1.20,38
-1.06,47
-2.19.4
-1 -1,87,58
-1.18.60
-0.88.0
1136
- 0.80
- 0.65
0.001.0
0.2553
0.50
0.7553
1,00
0.25 12
0.50
0.73
1.00"2
0.25
0.50
0.76
1.00
0.26
0.00"2
0.76
1.00"
0.25
0.50
0.76
1.00
0.25
6.77.00
-4.20.12
2.44
33.8.45
-0.96.45
-0.73.55
1,30
01
-5.20.10
-2.55,13
11.96.19
-1.37
2.02.2
1.58
-0.38
-0.15.41
-1.07
-1.11,
-0.10.66
-0.61
0.86
0.41
40.30
+0,02.00
0.75
3.55.11
-2.13.15
-0.92.21
-0.48.20
1.900
-0.96.20
-0.11,85
+0.48
-0 -0.90.47
+0.31
.1
-0.39.67
+8.62.60
+0.04 78
0.57
+0.45
-0. 04.14,97
1.00
3.22
-2.08
-0.85
-0.89 31
1.32
00.99
-0.85,37
+0.46
-0.80,40
+0,11 53
+0.33 60
+1.21 68
+0.11 80
+0.55 s5
+1.43
0.00,.0
ResNet110
8.25
0.50
0.75
1.00
0.25
0.50
0.75
1.00
0.25
0.50
0.75
1.00
0.25
0.50"
0.75
1.00
0.25
0.50
0.75
1.00
5.21.00
3.01,12
3.48.
2.44
3.71
-2.86
44.32,32
11220.40
-2.26.45
-7.35.49
49
-2.86,55
-1.95.0
6.50
33.40
-1.81, 87
-1.86, ac
01
1
22.70.13
12.6..19
0.84 27
23
2.59.
-2.35 33
22.1..1
-..4..46
-1.14,50
-1.31,50
6450..4
2.56.
0.86
-1.44,
-0.88.00
0.
33.26.11
-2.48.18
-1.50.20
-0.83.29
2..4.
-1.13.2
-1.83.44
-0.06.42
-1.72.48
2.433.1
-1.1
-0.52,
-0.99.
11
-2.90
-2.52
-1.50 -0.58
-188
-2.1
-0.33 36
-0.15, -3.99
-1.38,52
-5.08.sa
-0.81
2.08 -0.40 0.00
0.00,.0
Table Accuracy & Memory on CIFAR10. Each subtable lists all derived models for the denote table heading seed convnet.
ResNet20
k3
0.50
0.75
1.00
0.25
0.50
0.76
1.00
0.25
0.50
1.00
0.25
0.50
0.76
0.25
0.50
1.00
26.58,80r 09
21.09 13
17.49
-15.44.26
13.25 23
-9.55 27
8.18 as
-7.35 .1
7.33.44
-6.00.48
-3.66,56
-2.66.a
-6.79,r8
33.2
-1.94,8 85
-1.77 94
01
-23.02
19.14
1.6.83.2
-1
-11.21.24
-9.43.24
-7.33.3
-6.37.45
-5.81.40
-3.66.
-3.19.60
33.94,
-3.11
-1.91.6
-0.89.90
0.76
21.611
18.14.18
-15.37
-13.27.30
110.00.23
-.233.3
-7.06.36
-5.14.
6.00
-3.73
-.67
-3.27
-2.03
-1.48
-0.13.
1. 1.00
20.14.
-17.03.15
115.84
-12.98.32
-9.16
-7.00.3
6.07
6.22
4.90
-.24
-2.2
-2.1
-2.74.
-1.40
-0.53
0.001.0
RosNo144
0.25
0.
0.76
1.003
0.25
0.50%2
0.76
12
0.25
0.50
0.70
1.00
0.2g
0.50
0.76
1.00
0.25
0.50
0.70
1.00
01
-20.03.m 09
-17.50.12
-13.75.11 18
122.10
12
-9.69
-7.89.28
-5.80.32
-6.69.05
-4885.
-4.1
55
-195.63
-4.80
33.26
-1.53.a5
0.
-19.34,
-14.88.11
-12.32
110.08.27
6.855
-6.12
4.25
6.98
-400
-2.47
1992
-2.88
-2.42
-1,58
-0.97
91
16.78
11
-12.32
100.90.40
6.99
6.50
-6.96.3
4.11
4.44
2.67
22.8
1.89
-2.17
2.26
1..3
-0.37
1. 1.00
-16.39
13.61.1.
-11.36.25
-9.27
77.36
6.38
-4.83
3.56
4.64
- 2.38
22.4
1.73.68
-258.
2.76
-0.54
0.00,.0
k.25
0.50'h
0.75
1.00
0.25
0.50
0.75
1.00
0.25
0.50
0.75"4
1.00
0.25
0.50
0.76
1.00
0.25
0.50
0.75
1,00
01
122.64
-7.01,18
-7.00
66.07
0.69
40.21
1.72
-2.28
42,02
40.29
01
-10.26,
-8.44.13
-6.12.19
-3.58.22
-..77
-2.16.27
-0.54.33
-0.49.4
1.45.
-0.86 36.66.
-0.33.
+2.58
-10.51
-0.64
+1.64
+0.37
01
-9.16.11
-8.88.15
-5.68.
-4.51.0
33.45.m
- -0.96.0
- 3.59
+0.01
1.80
- 2.92
00:00-000000 -0.24.57
+0.43
+0.26 s0
-1.00
+2.13
+1.05
1.00e1
-9.23.
17
5.330,
4.62.
-3.66
1.844
-1.20.35
0.23
1.79,
+2.36
+2.00
+3.58 69
33.03
1..47
+1.29
0.001
Table 7: Accuracy & Memory CIFAR100. Each subtable lists all derived models for the denote table heading seed convnet.
13


Reviews:
[{"review_id": "WC5rQHDFL0_", "reviewer": "Reviewer_PfGt", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper demonstrates SCBP, a framework to compress convolutional networks, by first segmenting the layers into superblocks and then associate them with different compression ratios. The SCBP framework constructs a pool of models with different attributes that meet different needs.", "summary_of_the_review": "Overall, I think the work lacks novelty, research insights, experiments and analysis. It is not appropriate to be accepted in its current form.", "strength_and_weaknesses": "The method proposed is effective and not hard to follow.\nHowever, it brings limited new things/insights to me.\n1. The authors claim the core of its method is the segmentation and the c-ratio set.\nHowever, is the segmentation really needed?\nThe authors claim that the segmentation can reduce the search cost by binning similar layers together in a bucket/segment. It appears to me that this is very trivial. \n2. The c-ration, in the paper, is also trivial to set.\n3. Therefore, the framework is a bit of engineering with very limited research insight.\n4. A previous work, AutoSlim[1], associates a ratio to each single layer, and applies algorithm to automatically learn the ratio, which should be discussed and compared.\n5. The work does not compare to other methods.\n6. The experiment evaluation is also limited on only CIFAR.\n7. The authors claim there is no retrain cycle. However, after the compression, it requires to train and evaluated the candidates' architectures. This step is 'retrain' and would cost a lot of time.\n8. The resulted network size and FLOPS are not reported.\n9. The writing should be largely improved with many typos.\n\nReference:\n[1] https://arxiv.org/abs/1903.11728", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "1: The contributions are neither significant nor novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "VFNNVDiVeg", "reviewer": "Reviewer_yX2W", "summary": "", "questions": "", "limitations": "", "rating": 1, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper studies efficient neural network design for faster inference. The authors propose Structured Coarse Block Pruning (SCBP) that first partitions all layers into segments, then assigns a width multiplier to each stage, and finally explores the best configuration by grid search. The proposed SCBP delivers 20% faster and 50% smaller models on CIFAR-10 and CIFAR-100 benchmarks.", "summary_of_the_review": "My recommendation is based on the limited novelty, insufficient evaluation, and poor writing of this paper. Therefore, I think it is not ready for publication in its current shape.", "strength_and_weaknesses": "---\nStrengths:\n- This paper targets a critical problem: designing efficient neural architecture without much human effort and computational resources.\n\n---\nWeaknesses:\n- The proposed method is essentially a grid search over a simplified design space. The main contribution of this paper lies in partitioning layers into segments. However, the authors have provided no insights into why layers with the same feature map size should have the same width multiplier (i.e., pruning ratio). The current solution appears like an arbitrary design space simplification without proper justifications.\n- The paper is hard to follow. The algorithm boxes in Section 3 are composed of many sub-procedure (function) calls, which I find hard to understand without more clarification. Though the authors claim that they base their method on filter pruning, the proposed algorithm seems to retrain all the candidate networks from scratch without inheriting the weights from the original model.\n- This paper does not provide any baselines in its experimental evaluation. As the proposed method is highly related to pruning and neural architecture search, it is more than necessary to include some numbers of related baselines.\n- The experimental results are all on small-scale benchmarks, which is less representative. It would be essential to include some results on large-scale datasets, such as ImageNet. The authors claim that their search cost is much lower than other methods, which is actually because the dataset they are using is much smaller.\n\n---", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "1: The contributions are neither significant nor novel.", "empirical_novelty_and_significance": "1: The contributions are neither significant nor novel."}, "extra_text": {}}, {"review_id": "RHDDEeDWZR5", "reviewer": "Reviewer_aX8y", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The authors propose a systematic method of creating variations of a convolutional architecture by reducing the number of filters at certain groups of layers. Subsequently training and evaluating these new architectures shows that some are significantly more efficient that the initial \"seed\" architecture achieving similar accuracy scores with significantly less computational resources.", "summary_of_the_review": "Based on the lack of comparison with baselines as well as the low novelty I propose rejection.", "strength_and_weaknesses": "The method proposed in the paper requires retraining every variation from scratch which is not network compression but simply architecture search. Since there is very little information proposed to guide this architecture search the paper performs either brute force architecture search (using a grid search) or manual architecture search which is simply CNN training.\n\nTo strengthen the paper the authors should compare with network compression techniques (such as the ones listed in the related work section) and show whether the proposed method finds architectures with specific efficiency characteristics more efficiently than network compression.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "1: The main claims of the paper are incorrect or not at all supported by theory or empirical results.", "technical_novelty_and_significance": "1: The contributions are neither significant nor novel.", "empirical_novelty_and_significance": "1: The contributions are neither significant nor novel."}, "extra_text": {}}]

Author rebuttal:

Decision: Reject