1,720,969 research outputs found
CMix-NN: Mixed Low-Precision CNN Library for Memory-Constrained Edge Devices
Full text linkLow-precision integer arithmetic is a necessary ingredient for enabling Deep Learning inference on tiny and resource-constrained IoT edge devices. This brief presents CMix-NN, a flexible open-sourceCMix-NN is available at https://github.com/EEESlab/CMix-NN. mixed low-precision (independent tensors quantization of weight and activations at 8, 4, 2 bits) inference library for low bitwidth Quantized Networks. CMix-NN efficiently supports both Per-Layer and Per-Channel quantization strategies of weights and activations. Thanks to CMix-NN, we deploy on an STM32H7 microcontroller a set of Mobilenet family networks with the largest input resolutions ( 224 imes 224 ) and higher accuracies (up to 68% Top1) when compressed with a mixed low precision technique, achieving up to +8% accuracy improvement concerning any other published solution for MCU devices
Leveraging Automated Mixed-Low-Precision Quantization for Tiny Edge Microcontrollers
Full text linkThe severe on-chip memory limitations are currently preventing the deployment of the most accurate Deep Neural Network (DNN) models on tiny MicroController Units (MCUs), even if leveraging an effective 8-bit quantization scheme. To tackle this issue, in this paper we present an automated mixed-precision quantization flow based on the HAQ framework but tailored for the memory and computational characteristics of MCU devices. Specifically, a Reinforcement Learning agent searches for the best uniform quantization levels, among 2, 4, 8 bits, of individual weight and activation tensors, under the tight constraints on RAM and FLASH embedded memory sizes. We conduct an experimental analysis on MobileNetV1, MobileNetV2 and MNasNet models for Imagenet classification. Concerning the quantization policy search, the RL agent selects quantization policies that maximize the memory utilization. Given an MCU-class memory bound of 2 MB for weight-only quantization, the compressed models produced by the mixed-precision engine result as accurate as the state-of-the-art solutions quantized with a non-uniform function, which is not tailored for CPUs featuring integer-only arithmetic. This denotes the viability of uniform quantization, required for MCU deployments, for deep weights compression. When also limiting the activation memory budget to 512 kB, the best MobileNetV1 model scores up to 68.4% on Imagenet thanks to the found quantization policy, resulting to be 4% more accurate than the other 8-bit networks fitting the same memory constraints
Multi-resolution Rescored ByteTrack for Video Object Detection on Ultra-low-power Embedded Systems
No full textThis paper introduces Multi-Resolution Rescored ByteTrack (MR2-ByteTrack), a novel video object detection framework for ultra-low-power embedded processors. This method reduces the average compute load of an off-the-shelf Deep Neural Network (DNN) based object detector by up to 2.25× by alternating the processing of high-resolution images (320 × 320 pixels) with multiple down-sized frames (192×192 pixels). To tackle the accuracy degradation due to the reduced image input size, MR2-ByteTrack correlates the output detections over time using the ByteTrack tracker and corrects potential misclassification using a novel probabilistic Rescore algorithm. By interleaving two down-sized images for every high-resolution one as the input of different state-of-the-art DNN object detectors with our MR2-ByteTrack, we demonstrate an average accuracy increase of 2.16% and a latency reduction of 43% on the GAP9 microcontroller compared to a baseline frame-by-frame inference scheme using exclusively full-resolution images. Code available at: https://github.com/Bomps4/Multi-Resolution-Rescored-ByteTrac
PULP-NN: A computing library for quantized neural network inference at the edge on RISC-V based parallel ultra low power clusters
Full text linkWe present PULP-NN, a multicore computing library for a parallel ultra-low-power cluster of RISC-V based processors. The library consists of a set of kernels for Quantized Neural Network (QNN) inference on edge devices, targeting byte and sub-byte data types, down to INT-1. Our software solution exploits the digital signal processing (DSP) extensions available in the PULP RISC-V processors and the cluster's parallelism, improving performance by up to 63× with respect to a baseline implementation on a single RISC-V core implementing the RV32IMC ISA. Using the PULP-NN routines, the inference of a CIFAR-10 QNN model runs in 30× and 19.6× less clock cycles than the current state-of-the-art ARM CMSIS-NN library, running on an STM32L4 and an STM32H7 MCUs, respectively. By running the library kernels on the GAP-8 processor at the maximum efficiency operating point, the energy efficiency on GAP-8 is 14.1× higher than STM32L4 and 39.5× than STM32H7
An Energy Optimized JPEG Encoder for Parallel Ultra-Low-Power Processing-Platforms
No full textThe energy autonomy and the lifetime of battery-operated sensors are primary concerns in industrial, healthcare and IoT applications, in particular when a high amount of data needs to be sent wirelessly such as in Wireless Camera Sensors (WCS). Onboard real-time image compression is the appropriate solution to decrease the system’s energy. This paper proposes an optimized algorithm implementation tailored for PULP (Parallel Ultra Low Power) processors, that permits to shrink the image size and the data to transmit. Our optimized JPEG encoder based on a Fast-Discrete Cosine Transform (DCT) function is designed to achieve the best trade-off between energy consumption and image distortion. The parallel software implementation requires only 0.495 mJ per frame and can support up to 80 fps satisfying the most stringent requirements in WCSs applications without requiring a dedicated hardware accelerator
PULP-NN: Accelerating Quantized Neural Networks on Parallel Ultra-Low-Power RISC-V Processors
Full text linkWe present PULP-NN, an optimized computing library for a parallel ultra-low-power tightly coupled cluster of RISC-V processors. The key innovation in PULP-NN is a set of kernels for quantized neural network inference, targeting byte and sub-byte data types, down to INT-1, tuned for the recent trend toward aggressive quantization in deep neural network inference. The proposed library exploits both the digital signal processing extensions available in the PULP RISC-V processors and the cluster’s parallelism, achieving up to 15.5 MACs/cycle on INT-8 and improving performance by up to 63× with respect to a sequential implementation on a single RISC-V core implementing the baseline RV32IMC ISA. Using PULP-NN, a CIFAR-10 network on an octa-core cluster runs in 30× and 19.6× less clock cycles than the current state-of-the-art ARM CMSIS-NN library, running on STM32L4 and STM32H7 MCUs, respectively. The proposed library, when running on a GAP-8 processor, outperforms by 36.8× and by 7.45× the execution on energy efficient MCUs such as STM32L4 and high-end MCUs such as STM32H7 respectively, when operating at the maximum frequency. The energy efficiency on GAP-8 is 14.1× higher than STM32L4 and 39.5× higher than STM32H7, at the maximum efficiency operating point. This article is part of the theme issue ‘Harmonizing energy-autonomous computing and intelligence’
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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