888 research outputs found
ARCHANGEL: Tamper-proofing Video Archives using Temporal Content Hashes on the Blockchain
We present ARCHANGEL; a novel distributed ledger based system for assuring
the long-term integrity of digital video archives. First, we describe a novel
deep network architecture for computing compact temporal content hashes (TCHs)
from audio-visual streams with durations of minutes or hours. Our TCHs are
sensitive to accidental or malicious content modification (tampering) but
invariant to the codec used to encode the video. This is necessary due to the
curatorial requirement for archives to format shift video over time to ensure
future accessibility. Second, we describe how the TCHs (and the models used to
derive them) are secured via a proof-of-authority blockchain distributed across
multiple independent archives. We report on the efficacy of ARCHANGEL within
the context of a trial deployment in which the national government archives of
the United Kingdom, Estonia and Norway participated.Comment: Accepted to CVPR Blockchain Workshop 201
Run-Time Efficient RNN Compression for Inference on Edge Devices
Recurrent neural networks can be large and compute-intensive, yet many
applications that benefit from RNNs run on small devices with very limited
compute and storage capabilities while still having run-time constraints. As a
result, there is a need for compression techniques that can achieve significant
compression without negatively impacting inference run-time and task accuracy.
This paper explores a new compressed RNN cell implementation called Hybrid
Matrix Decomposition (HMD) that achieves this dual objective. This scheme
divides the weight matrix into two parts - an unconstrained upper half and a
lower half composed of rank-1 blocks. This results in output features where the
upper sub-vector has "richer" features while the lower-sub vector has
"constrained features". HMD can compress RNNs by a factor of 2-4x while having
a faster run-time than pruning (Zhu &Gupta, 2017) and retaining more model
accuracy than matrix factorization (Grachev et al., 2017). We evaluate this
technique on 5 benchmarks spanning 3 different applications, illustrating its
generality in the domain of edge computing.Comment: Published at 4th edition of Workshop on Energy Efficient Machine
Learning and Cognitive Computing for Embedded Applications at International
Symposium of Computer Architecture 2019, Phoenix, Arizona
(https://www.emc2-workshop.com/isca-19) colocated with ISCA 201
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