Concept
Tensor Processing Unit
Publications associées (73)
DBFS: Dynamic Bitwidth-Frequency Scaling for Efficient Software-defined SIMD
Giovanni Ansaloni, Alexandre Sébastien Julien Levisse, Pengbo Yu, Flavio Ponzina
Machine learning algorithms such as Convolutional Neural Networks (CNNs) are characterized by high robustness towards quantization, supporting small-bitwidth fixed-point arithmetic at inference time with little to no degradation in accuracy. In turn, small ...
2024FEMU: An Open-Source RISC-V Emulation Platform for the Exploration of Accelerator-based Edge Applications
David Atienza Alonso, Miguel Peon Quiros, José Angel Miranda Calero, Simone Machetti, Pasquale Davide Schiavone, Juan Pablo Sapriza Araujo, Deniz Kasap, Ruben Rodriguez
In this work, we present FEMU, an open-source RISC-V emulation platform to support the exploration of accelerator-based edge applications. ...
2024COMMUNICATION LOWER BOUNDS AND OPTIMAL ALGORITHMS FOR MULTIPLE TENSOR-TIMES-MATRIX COMPUTATION
Multiple tensor-times-matrix (Multi-TTM) is a key computation in algorithms for computing and operating with the Tucker tensor decomposition, which is frequently used in multidimensional data analysis. We establish communication lower bounds that determine ...
Philadelphia2024Robot Learning using Tensor Networks
In various robotics applications, the selection of function approximation methods greatly influences the feasibility and computational efficiency of algorithms. Tensor Networks (TNs), also referred to as tensor decomposition techniques, present a versatile ...
EPFL2024STREAMING TENSOR TRAIN APPROXIMATION
Tensor trains are a versatile tool to compress and work with high-dimensional data and functions. In this work we introduce the streaming tensor train approximation (STTA), a new class of algorithms for approximating a given tensor ' in the tensor train fo ...
Philadelphia2023Design Space Exploration for Partitioning Dataflow Program on CPU-GPU Heterogeneous System
Marco Mattavelli, Simone Casale Brunet, Aurélien François Gilbert Bloch
Dataflow programming is a methodology that enables the development of high-level, parametric programs that are independent of the underlying platform. This approach is particularly useful for heterogeneous platforms, as it eliminates the need to rewrite ap ...
SPRINGER2023