Search Results for author:
Found 13 papers, 6 papers with code
ApproxDARTS: Differentiable Neural Architecture Search with Approximate Multipliers
Integrating the principles of approximate computing into the design of hardware-aware deep neural networks (DNN) has led to DNNs implementations showing good output quality and highly optimized hardware parameters such as low latency or inference energy.
Exploring Quantization and Mapping Synergy in Hardware-Aware Deep Neural Network Accelerators
Energy efficiency and memory footprint of a convolutional neural network (CNN) implemented on a CNN inference accelerator depend on many factors, including a weight quantization strategy (i. e., data types and bit-widths) and mapping (i. e., placement and scheduling of DNN elementary operations on hardware units of the accelerator).
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.
Evolutionary Neural Architecture Search Supporting Approximate Multipliers
During the NAS process, a suitable CNN architecture is evolved together with approximate multipliers to deliver the best trade-offs between the accuracy, network size, and power consumption.
DESCNet: Developing Efficient Scratchpad Memories for Capsule Network Hardware
We analyze the corresponding on-chip memory requirements and leverage it to propose a novel methodology to explore different scratchpad memory designs and their energy/area trade-offs.
NASCaps: A Framework for Neural Architecture Search to Optimize the Accuracy and Hardware Efficiency of Convolutional Capsule Networks
Deep Neural Networks (DNNs) have made significant improvements to reach the desired accuracy to be employed in a wide variety of Machine Learning (ML) applications.
Semantically-Oriented Mutation Operator in Cartesian Genetic Programming for Evolutionary Circuit Design
Considering the multiplier design problem, for example, the 5x5-bit multiplier represents the most complex circuit evolved from a randomly generated initial population.
Adaptive Verifiability-Driven Strategy for Evolutionary Approximation of Arithmetic Circuits
We present a novel approach for designing complex approximate arithmetic circuits that trade correctness for power consumption and play important role in many energy-aware applications.
TFApprox: Towards a Fast Emulation of DNN Approximate Hardware Accelerators on GPU
In order to address this issue, we propose an efficient emulation method for approximate circuits utilized in a given DNN accelerator which is emulated on GPU.
ReD-CaNe: A Systematic Methodology for Resilience Analysis and Design of Capsule Networks under Approximations
To the best of our knowledge, this is the first proof-of-concept for employing approximations on the specialized CapsNet hardware.
ALWANN: Automatic Layer-Wise Approximation of Deep Neural Network Accelerators without Retraining
A suitable approximate multiplier is then selected for each computing element from a library of approximate multipliers in such a way that (i) one approximate multiplier serves several layers, and (ii) the overall classification error and energy consumption are minimized.
Automated Circuit Approximation Method Driven by Data Distribution
We propose an application-tailored data-driven fully automated method for functional approximation of combinational circuits.
autoAx: An Automatic Design Space Exploration and Circuit Building Methodology utilizing Libraries of Approximate Components
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.
