347 research outputs found
Is Conventional SNN Really Efficient? A Perspective from Network Quantization
Spiking Neural Networks (SNNs) have been widely praised for their high energy
efficiency and immense potential. However, comprehensive research that
critically contrasts and correlates SNNs with quantized Artificial Neural
Networks (ANNs) remains scant, often leading to skewed comparisons lacking
fairness towards ANNs. This paper introduces a unified perspective,
illustrating that the time steps in SNNs and quantized bit-widths of activation
values present analogous representations. Building on this, we present a more
pragmatic and rational approach to estimating the energy consumption of SNNs.
Diverging from the conventional Synaptic Operations (SynOps), we champion the
"Bit Budget" concept. This notion permits an intricate discourse on
strategically allocating computational and storage resources between weights,
activation values, and temporal steps under stringent hardware constraints.
Guided by the Bit Budget paradigm, we discern that pivoting efforts towards
spike patterns and weight quantization, rather than temporal attributes,
elicits profound implications for model performance. Utilizing the Bit Budget
for holistic design consideration of SNNs elevates model performance across
diverse data types, encompassing static imagery and neuromorphic datasets. Our
revelations bridge the theoretical chasm between SNNs and quantized ANNs and
illuminate a pragmatic trajectory for future endeavors in energy-efficient
neural computations
Fault feature extraction method based on EWT-SMF and MF-DFA for valve fault of reciprocating compressor
According to the nonlinearity and nonstationarity characteristics of reciprocating compressor vibration signal, a fault feature extraction method of reciprocating compressor based on the empirical wavelet transform (EWT) and state-adaptive morphological filtering (SMF) is proposed. Firstly, an adaptive empirical wavelet transform was used to divide the Fourier spectrum by constructing a scale-space curve, and an appropriate orthogonal wavelet filter bank was constructed to extract the AM-FM component with a tightly-supported Fourier spectrum. Then according to the impact characteristic of the reciprocating compressor vibration signal, the morphological structural elements were constructed with the characteristics of the signal to perform state-adaptive morphological filtering on the partitioned modal functions. Finally, the MF-DFA method of the modal function was quantitatively analyzed and the fault identification was performed. By analyzing the experimental data, it can be shown that the method can effectively identify the fault type of reciprocating compressor valve
Docking system design and self-assembly control of distributed swarm flying robots
This paper presents a novel docking system design and the distributed self-assembly control strategy for a Distributed Swarm Flying Robot (DSFR). The DSFR is a swarm robot comprising many identical robot modules that are able to move on the ground, dock with each other and fly coordinately once self-assembled into a robotic structure. A generalized adjacency matrix method is proposed to describe the configurations of robotic structures. Based on the docking system and the adjacency matrix, experiments are performed to demonstrate and verify the self-assembly control strategy
FireFly v2: Advancing Hardware Support for High-Performance Spiking Neural Network with a Spatiotemporal FPGA Accelerator
Spiking Neural Networks (SNNs) are expected to be a promising alternative to
Artificial Neural Networks (ANNs) due to their strong biological
interpretability and high energy efficiency. Specialized SNN hardware offers
clear advantages over general-purpose devices in terms of power and
performance. However, there's still room to advance hardware support for
state-of-the-art (SOTA) SNN algorithms and improve computation and memory
efficiency. As a further step in supporting high-performance SNNs on
specialized hardware, we introduce FireFly v2, an FPGA SNN accelerator that can
address the issue of non-spike operation in current SOTA SNN algorithms, which
presents an obstacle in the end-to-end deployment onto existing SNN hardware.
To more effectively align with the SNN characteristics, we design a
spatiotemporal dataflow that allows four dimensions of parallelism and
eliminates the need for membrane potential storage, enabling on-the-fly spike
processing and spike generation. To further improve hardware acceleration
performance, we develop a high-performance spike computing engine as a backend
based on a systolic array operating at 500-600MHz. To the best of our
knowledge, FireFly v2 achieves the highest clock frequency among all FPGA-based
implementations. Furthermore, it stands as the first SNN accelerator capable of
supporting non-spike operations, which are commonly used in advanced SNN
algorithms. FireFly v2 has doubled the throughput and DSP efficiency when
compared to our previous version of FireFly and it exhibits 1.33 times the DSP
efficiency and 1.42 times the power efficiency compared to the current most
advanced FPGA accelerators
FireFly: A High-Throughput and Reconfigurable Hardware Accelerator for Spiking Neural Networks
Spiking neural networks (SNNs) have been widely used due to their strong
biological interpretability and high energy efficiency. With the introduction
of the backpropagation algorithm and surrogate gradient, the structure of
spiking neural networks has become more complex, and the performance gap with
artificial neural networks has gradually decreased. However, most SNN hardware
implementations for field-programmable gate arrays (FPGAs) cannot meet
arithmetic or memory efficiency requirements, which significantly restricts the
development of SNNs. They do not delve into the arithmetic operations between
the binary spikes and synaptic weights or assume unlimited on-chip RAM
resources by using overly expensive devices on small tasks. To improve
arithmetic efficiency, we analyze the neural dynamics of spiking neurons,
generalize the SNN arithmetic operation to the multiplex-accumulate operation,
and propose a high-performance implementation of such operation by utilizing
the DSP48E2 hard block in Xilinx Ultrascale FPGAs. To improve memory
efficiency, we design a memory system to enable efficient synaptic weights and
membrane voltage memory access with reasonable on-chip RAM consumption.
Combining the above two improvements, we propose an FPGA accelerator that can
process spikes generated by the firing neuron on-the-fly (FireFly). FireFly is
implemented on several FPGA edge devices with limited resources but still
guarantees a peak performance of 5.53TSOP/s at 300MHz. As a lightweight
accelerator, FireFly achieves the highest computational density efficiency
compared with existing research using large FPGA devices
Do DALL-E and Flamingo Understand Each Other?
The field of multimodal research focusing on the comprehension and creation
of both images and text has witnessed significant strides. This progress is
exemplified by the emergence of sophisticated models dedicated to image
captioning at scale, such as the notable Flamingo model and text-to-image
generative models, with DALL-E serving as a prominent example. An interesting
question worth exploring in this domain is whether Flamingo and DALL-E
understand each other. To study this question, we propose a reconstruction task
where Flamingo generates a description for a given image and DALL-E uses this
description as input to synthesize a new image. We argue that these models
understand each other if the generated image is similar to the given image.
Specifically, we study the relationship between the quality of the image
reconstruction and that of the text generation. We find that an optimal
description of an image is one that gives rise to a generated image similar to
the original one. The finding motivates us to propose a unified framework to
finetune the text-to-image and image-to-text models. Concretely, the
reconstruction part forms a regularization loss to guide the tuning of the
models. Extensive experiments on multiple datasets with different image
captioning and image generation models validate our findings and demonstrate
the effectiveness of our proposed unified framework. As DALL-E and Flamingo are
not publicly available, we use Stable Diffusion and BLIP in the remaining work.
Project website: https://dalleflamingo.github.io.Comment: Accepted to ICCV 202
iTRAQ-Based Comparative Proteomic Analysis Reveals Molecular Mechanisms Underlying Wing Dimorphism of the Pea Aphid, Acyrthosiphon pisum
Wing dimorphism is a widespread phenomenon in insects with an associated trade-off between flight capability and fecundity. Despite the molecular underpinnings of phenotypic plasticity that has already been elucidated, it is still not fully understood. In this study, we focused on the differential proteomics profiles between alate and apterous morphs of the pea aphid, Acyrthosiphon pisum at the fourth instar nymph and adult stages, using isobaric tags for relative and absolute quantitation (iTRAQ) in a proteomic-based approach. A total of 5,116 protein groups were identified and quantified in the three biological replicates, of which 836 were differentially expressed between alate and apterous morphs. A bioinformatics analysis of differentially expressed protein groups (DEPGs) was performed based on gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). KEGG enrichment analysis showed that DEPGs mainly participated in energy metabolism, amino acid biosynthesis and metabolism, and signal sensing and transduction. To verify the reliability of proteomics data, the transcriptional expression of 29 candidates of differentially expressed proteins were analyzed by quantitative real-time PCR (qRT-PCR), showing that 26 genes were consistent with those at proteomic levels. In addition, differentially expressed proteins between winged and wingless morphs that were linked to olfactory sense were investigated. Quantitative real-time PCR revealed the tissue- and morph-biased expression profiles. These results suggested that olfactory sense plays a key role in wing dimorphism of aphids. The comparative proteomic analysis between alate and apterous morphs of the pea aphid provides a novel insight into wing development and dimorphism in aphids and will help facilitate our understanding of these concepts at molecular levels
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A photo-responsive F-box protein FOF2 regulates floral initiation by promoting FLC expression in Arabidopsis.
Floral initiation is regulated by various genetic pathways in response to light, temperature, hormones and developmental status; however, the molecular mechanisms underlying the interactions between different genetic pathways are not fully understood. Here, we show that the photoresponsive gene FOF2 (F-box of flowering 2) negatively regulates flowering. FOF2 encodes a putative F-box protein that interacts specifically with ASK14, and its overexpression results in later flowering under both long-day and short-day photoperiods. Conversely, transgenic plants expressing the F-box domain deletion mutant of FOF2 (FOF2ΔF), or double loss of function mutant of FOF2 and FOL1 (FOF2-LIKE 1) present early flowering phenotypes. The late flowering phenotype of the FOF2 overexpression lines is suppressed by the flc-3 loss-of-function mutation. Furthermore, FOF2 mRNA expression is regulated by autonomous pathway gene FCA, and the repressive effect of FOF2 in flowering can be overcome by vernalization. Interestingly, FOF2 expression is regulated by light. The protein level of FOF2 accumulates in response to light, whereas it is degraded under dark conditions via the 26S proteasome pathway. Our findings suggest a possible mechanistic link between light conditions and the autonomous floral promotion pathway in Arabidopsis
Paths to light trapping in thin film GaAs solar cells
It is now well established that light trapping is an essential element of thin film solar cell design. Numerous light trapping geometries have already been applied to thin film cells, especially to silicon-based devices. Less attention has been paid to light trapping in GaAs thin film cells, mainly because light trapping is considered less attractive due to the material's direct bandgap and the fact that GaAs suffers from strong surface recombination, which particularly affects etched nanostructures. Here, we study light trapping structures that are implemented in a high-bandgap material on the back of the GaAs active layer, thereby not perturbing the integrity of the GaAs active layer. We study photonic crystal and quasi-random nanostructures both by simulation and by experiment and find that the photonic crystal structures are superior because they exhibit fewer but stronger resonances that are better matched to the narrow wavelength range where GaAs benefits from light trapping. In fact, we show that a 1500 nm thick cell with photonic crystals achieves the same short circuit current as an unpatterned 4000 nm thick cell. These findings are significant because they afford a sizeable reduction in active layer thickness, and therefore a reduction in expensive epitaxial growth time and cost, yet without compromising performance
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