1,321 research outputs found
Natuur past goed op intensief melkveebedrijf
Nadelige effecten op de bedrijfsvoering zijn er nauwelijks, het extra werk is beperkt. Natuur begint zich geleidelijk te ontwikkelen
Memory and Parallelism Analysis Using a Platform-Independent Approach
Emerging computing architectures such as near-memory computing (NMC) promise
improved performance for applications by reducing the data movement between CPU
and memory. However, detecting such applications is not a trivial task. In this
ongoing work, we extend the state-of-the-art platform-independent software
analysis tool with NMC related metrics such as memory entropy, spatial
locality, data-level, and basic-block-level parallelism. These metrics help to
identify the applications more suitable for NMC architectures.Comment: 22nd ACM International Workshop on Software and Compilers for
Embedded Systems (SCOPES '19), May 201
Reservaatbeheer met zoogkoeien kost geld
Bij de huidige vleesprijzen zijn de opbrengsten van de vleesveehouderij laag en kan een zoogkoeienhouder geen pacht betalen, maar moet hij een vergoeding krijgen om met het begrazen van natuurterreinen een inkomen te halen
How to train accurate BNNs for embedded systems?
A key enabler of deploying convolutional neural networks on
resource-constrained embedded systems is the binary neural network (BNN). BNNs
save on memory and simplify computation by binarizing both features and
weights. Unfortunately, binarization is inevitably accompanied by a severe
decrease in accuracy. To reduce the accuracy gap between binary and
full-precision networks, many repair methods have been proposed in the recent
past, which we have classified and put into a single overview in this chapter.
The repair methods are divided into two main branches, training techniques and
network topology changes, which can further be split into smaller categories.
The latter category introduces additional cost (energy consumption or
additional area) for an embedded system, while the former does not. From our
overview, we observe that progress has been made in reducing the accuracy gap,
but BNN papers are not aligned on what repair methods should be used to get
highly accurate BNNs. Therefore, this chapter contains an empirical review that
evaluates the benefits of many repair methods in isolation over the
ResNet-20\&CIFAR10 and ResNet-18\&CIFAR100 benchmarks. We found three repair
categories most beneficial: feature binarizer, feature normalization, and
double residual. Based on this review we discuss future directions and research
opportunities. We sketch the benefit and costs associated with BNNs on embedded
systems because it remains to be seen whether BNNs will be able to close the
accuracy gap while staying highly energy-efficient on resource-constrained
embedded systems
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