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06/28/2023
ReDy: A Novel ReRAM-centric Dynamic Quantization Approach for Energy-efficient CNN Inference
The primary operation in DNNs is the dot product of quantized input acti...
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06/28/2023
DNA-TEQ: An Adaptive Exponential Quantization of Tensors for DNN Inference
Quantization is commonly used in Deep Neural Networks (DNNs) to reduce t...
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02/01/2023
K-D Bonsai: ISA-Extensions to Compress K-D Trees for Autonomous Driving Tasks
Autonomous Driving (AD) systems extensively manipulate 3D point clouds f...
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12/01/2022
Exploiting Kernel Compression on BNNs
Binary Neural Networks (BNNs) are showing tremendous success on realisti...
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02/21/2022
Dynamic Sampling Rate: Harnessing Frame Coherence in Graphics Applications for Energy-Efficient GPUs
In real-time rendering, a 3D scene is modelled with meshes of triangles ...
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02/14/2022
Saving RNN Computations with a Neuron-Level Fuzzy Memoization Scheme
Recurrent Neural Networks (RNNs) are a key technology for applications s...
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02/10/2022
Mixture-of-Rookies: Saving DNN Computations by Predicting ReLU Outputs
Deep Neural Networks (DNNs) are widely used in many applications domains...
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02/10/2022
ASRPU: A Programmable Accelerator for Low-Power Automatic Speech Recognition
The outstanding accuracy achieved by modern Automatic Speech Recognition...
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12/23/2021
A Survey of Near-Data Processing Architectures for Neural Networks
Data-intensive workloads and applications, such as machine learning (ML)...
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07/20/2021
CREW: Computation Reuse and Efficient Weight Storage for Hardware-accelerated MLPs and RNNs
Deep Neural Networks (DNNs) have achieved tremendous success for cogniti...
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02/26/2021
A Variable Vector Length SIMD Architecture for HW/SW Co-designed Processors
Hardware/Software (HW/SW) co-designed processors provide a promising sol...
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01/22/2021
Exploiting Beam Search Confidence for Energy-Efficient Speech Recognition
With computers getting more and more powerful and integrated in our dail...
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09/22/2020
E-BATCH: Energy-Efficient and High-Throughput RNN Batching
Recurrent Neural Network (RNN) inference exhibits low hardware utilizati...
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07/14/2020
Irregular Accesses Reorder Unit: Improving GPGPU Memory Coalescing for Graph-Based Workloads
GPGPU architectures have become established as the dominant parallelizat...
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11/07/2019
Boosting LSTM Performance Through Dynamic Precision Selection
The use of low numerical precision is a fundamental optimization include...
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11/04/2019
LSTM-Sharp: An Adaptable, Energy-Efficient Hardware Accelerator for Long Short-Term Memory
The effectiveness of LSTM neural networks for popular tasks such as Auto...
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06/06/2019
(Pen-) Ultimate DNN Pruning
DNN pruning reduces memory footprint and computational work of DNN-based...
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07/25/2018
Rendering Elimination: Early Discard of Redundant Tiles in the Graphics Pipeline
GPUs are one of the most energy-consuming components for real-time rende...
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01/16/2018
Trends in Processor Architecture
This paper presents an overview of the main trends in processor architec...
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11/20/2017
