16 research outputs found
Automated Circuit Approximation Method Driven by Data Distribution
We propose an application-tailored data-driven fully automated method for
functional approximation of combinational circuits. We demonstrate how an
application-level error metric such as the classification accuracy can be
translated to a component-level error metric needed for an efficient and fast
search in the space of approximate low-level components that are used in the
application. This is possible by employing a weighted mean error distance
(WMED) metric for steering the circuit approximation process which is conducted
by means of genetic programming. WMED introduces a set of weights (calculated
from the data distribution measured on a selected signal in a given
application) determining the importance of each input vector for the
approximation process. The method is evaluated using synthetic benchmarks and
application-specific approximate MAC (multiply-and-accumulate) units that are
designed to provide the best trade-offs between the classification accuracy and
power consumption of two image classifiers based on neural networks.Comment: Accepted for publication at Design, Automation and Test in Europe
(DATE 2019). Florence, Ital
autoAx: An Automatic Design Space Exploration and Circuit Building Methodology utilizing Libraries of Approximate Components
Approximate computing is an emerging paradigm for developing highly
energy-efficient computing systems such as various accelerators. In the
literature, many libraries of elementary approximate circuits have already been
proposed to simplify the design process of approximate accelerators. Because
these libraries contain from tens to thousands of approximate implementations
for a single arithmetic operation it is intractable to find an optimal
combination of approximate circuits in the library even for an application
consisting of a few operations. An open problem is "how to effectively combine
circuits from these libraries to construct complex approximate accelerators".
This paper proposes a novel methodology for searching, selecting and combining
the most suitable approximate circuits from a set of available libraries to
generate an approximate accelerator for a given application. To enable fast
design space generation and exploration, the methodology utilizes machine
learning techniques to create computational models estimating the overall
quality of processing and hardware cost without performing full synthesis at
the accelerator level. Using the methodology, we construct hundreds of
approximate accelerators (for a Sobel edge detector) showing different but
relevant tradeoffs between the quality of processing and hardware cost and
identify a corresponding Pareto-frontier. Furthermore, when searching for
approximate implementations of a generic Gaussian filter consisting of 17
arithmetic operations, the proposed approach allows us to identify
approximately highly important implementations from possible
solutions in a few hours, while the exhaustive search would take four months on
a high-end processor.Comment: Accepted for publication at the Design Automation Conference 2019
(DAC'19), Las Vegas, Nevada, US
Xel-FPGAs: An End-to-End Automated Exploration Framework for Approximate Accelerators in FPGA-Based Systems
Generation and exploration of approximate circuits and accelerators has been
a prominent research domain to achieve energy-efficiency and/or performance
improvements. This research has predominantly focused on ASICs, while not
achieving similar gains when deployed for FPGA-based accelerator systems, due
to the inherent architectural differences between the two. In this work, we
propose a novel framework, Xel-FPGAs, which leverages statistical or machine
learning models to effectively explore the architecture-space of
state-of-the-art ASIC-based approximate circuits to cater them for FPGA-based
systems given a simple RTL description of the target application. We have also
evaluated the scalability of our framework on a multi-stage application using a
hierarchical search strategy. The Xel-FPGAs framework is capable of reducing
the exploration time by up to 95%, when compared to the default synthesis,
place, and route approaches, while identifying an improved set of
Pareto-optimal designs for a given application, when compared to the
state-of-the-art. The complete framework is open-source and available online at
https://github.com/ehw-fit/xel-fpgas.Comment: Accepted for publication at the 42nd International Conference on
Computer-Aided Design (ICCAD), November 2023, San Francisco, CA, US
RoHNAS: A Neural Architecture Search Framework with Conjoint Optimization for Adversarial Robustness and Hardware Efficiency of Convolutional and Capsule Networks
Neural Architecture Search (NAS) algorithms aim at finding efficient Deep Neural Network (DNN) architectures for a given application under given system constraints. DNNs are computationally-complex as well as vulnerable to adversarial attacks. In order to address multiple design objectives, we propose RoHNAS , a novel NAS framework that jointly optimizes for adversarial-robustness and hardware-efficiency of DNNs executed on specialized hardware accelerators. Besides the traditional convolutional DNNs, RoHNAS additionally accounts for complex types of DNNs such as Capsule Networks. For reducing the exploration time, RoHNAS analyzes and selects appropriate values of adversarial perturbation for each dataset to employ in the NAS flow. Extensive evaluations on multi - Graphics Processing Unit (GPU) - High Performance Computing (HPC) nodes provide a set of Pareto-optimal solutions, leveraging the tradeoff between the above-discussed design objectives. For example, a Pareto-optimal DNN for the CIFAR-10 dataset exhibits 86.07% accuracy, while having an energy of 38.63 mJ, a memory footprint of 11.85 MiB, and a latency of 4.47 ms
Variation in honey bee gut microbial diversity affected by ontogenetic stage, age and geographic location
Social honey bees, Apis mellifera, host a set of distinct microbiota, which is similar across the continents and various honey bee species. Some of these bacteria, such as lactobacilli, have been linked to immunity and defence against pathogens. Pathogen defence is crucial, particularly in larval stages, as many pathogens affect the brood. However, information on larval microbiota is conflicting.
Seven developmental stages and drones were sampled from 3 colonies at each of the 4 geographic locations of A. mellifera carnica, and the samples were maintained separately for analysis. We analysed the variation and abundance of important bacterial groups and taxa in the collected bees.
Major bacterial groups were evaluated over the entire life of honey bee individuals, where digestive tracts of same aged bees were sampled in the course of time. The results showed that the microbial tract of 6-day-old 5th instar larvae were nearly equally rich in total microbial counts per total digestive tract weight as foraging bees, showing a high percentage of various lactobacilli (Firmicutes) and Gilliamella apicola (Gammaproteobacteria 1). However, during pupation, microbial counts were significantly reduced but recovered quickly by 6 days post-emergence. Between emergence and day 6, imago reached the highest counts of Firmicutes and Gammaproteobacteria, which then gradually declined with bee age. Redundancy analysis conducted using denaturing gradient gel electrophoresis identified bacterial species that were characteristic of each developmental stage.
The results suggest that 3-day 4th instar larvae contain low microbial counts that increase 2-fold by day 6 and then decrease during pupation. Microbial succession of the imago begins soon after emergence. We found that bacterial counts do not show only yearly cycles within a colony, but vary on the individual level. Sampling and pooling adult bees or 6th day larvae may lead to high errors and variability, as both of these stages may be undergoing dynamic succession
Optimization of BDD-based Approximation Error Metrics Calculations
Software methods introduced for automated design of approximate
implementations of arithmetic circuits rely on fast and accurate evaluation of
approximate candidate implementations. To accelerate the evaluation of circuit
error, we propose four novel algorithms for the exact worst-case and mean
absolute error analysis based on Binary Decision Diagrams. As these algorithms
do not compute any absolute values in the characteristic function, which
basically compares a candidate approximate circuit with a golden circuit, the
error evaluation is significantly faster than the standard BDD-based error
analysis. On average, the proposed algorithms are three times faster (in some
cases, 30 times faster) than the baseline for 8- to 32-bit approximate adders.
These results were obtained from more than 49 thousand runs with different
configurations of the method. The proposed error evaluation algorithms are
available as an open-source software https://github.com/ehw-fit/bdd-evaluation.Comment: To appear at the 2022 IEEE Computer Society Annual Symposium on VLSI
- ISVLSI 202