100 research outputs found
DESIGN AND CHARACTERIZATION OF LOW-POWER LOW-NOISE ALLDIGITAL SERIAL LINK FOR POINT-TO-POINT COMMUNICATION IN SOC
The fully-digital implementation of serial links has recently emerged as a viable
alternative to their classical analogue counterpart. Indeed, reducing the analogue
content in favour of expanding the digital content becomes more attractive due to the
ability to achieve less power consumption, less sensitivity to the noise and better
scalability across multiple technologies and platforms with inconsiderable
modifications. In addition, describing the circuit in hardware description languages
gives it a high flexibility to program all design parameters in a very short time
compared with the analogue designs which need to be re-designed at transistor level
for any parameter change. This can radically reduce cost and time-to-market by
saving a significant amount of development time. However, beside these considerable
advantages, the fully-digital architecture poses several design challenges
A Framework for Designing Efficient Deep Learning-Based Genomic Basecallers
Nanopore sequencing generates noisy electrical signals that need to be
converted into a standard string of DNA nucleotide bases using a computational
step called basecalling. The accuracy and speed of basecalling have critical
implications for all later steps in genome analysis. Many researchers adopt
complex deep learning-based models to perform basecalling without considering
the compute demands of such models, which leads to slow, inefficient, and
memory-hungry basecallers. Therefore, there is a need to reduce the computation
and memory cost of basecalling while maintaining accuracy. Our goal is to
develop a comprehensive framework for creating deep learning-based basecallers
that provide high efficiency and performance. We introduce RUBICON, a framework
to develop hardware-optimized basecallers. RUBICON consists of two novel
machine-learning techniques that are specifically designed for basecalling.
First, we introduce the first quantization-aware basecalling neural
architecture search (QABAS) framework to specialize the basecalling neural
network architecture for a given hardware acceleration platform while jointly
exploring and finding the best bit-width precision for each neural network
layer. Second, we develop SkipClip, the first technique to remove the skip
connections present in modern basecallers to greatly reduce resource and
storage requirements without any loss in basecalling accuracy. We demonstrate
the benefits of RUBICON by developing RUBICALL, the first hardware-optimized
basecaller that performs fast and accurate basecalling. Compared to the fastest
state-of-the-art basecaller, RUBICALL provides a 3.96x speedup with 2.97%
higher accuracy. We show that RUBICON helps researchers develop
hardware-optimized basecallers that are superior to expert-designed models
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