Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DNN is not all you need: Parallelizing Non-Neural ML Algorithms on
  Ultra-Low-Power IoT Processors   Machine Learning (ML) functions are becoming ubiquitous in latency- and
privacy-sensitive IoT applications, prompting for a shift toward near-sensor
processing at the extreme edge and the consequent increasing adoption of
Parallel Ultra-Low Power (PULP) IoT processors. These compute- and
memory-constrained parallel architectures need to run efficiently a wide range
of algorithms, including key Non-Neural ML kernels that compete favorably with
Deep Neural Networks (DNNs) in terms of accuracy under severe resource
constraints. In this paper, we focus on enabling efficient parallel execution
of Non-Neural ML algorithms on two RISCV-based PULP platforms, namely GAP8, a
commercial chip, and PULP-OPEN, a research platform running on an FPGA
emulator. We optimized the parallel algorithms through a fine-grained analysis
and intensive optimization to maximize the speedup, considering two alternative
Floating-Point (FP) emulation libraries on GAP8 and the native FPU support on
PULP-OPEN. Experimental results show that a target-optimized emulation library
can lead to an average 1.61x runtime improvement compared to a standard
emulation library, while the native FPU support reaches up to 32.09x. In terms
of parallel speedup, our design improves the sequential execution by 7.04x on
average on the targeted octa-core platforms. Lastly, we present a comparison
with the ARM Cortex-M4 microcontroller (MCU), a widely adopted commercial
solution for edge deployments, which is 12.87$x slower than PULP-OPEN.

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The categories of this paper are: nucl-th
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The categories of this paper are: supr-con
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
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The categories of this paper are: ao-sci
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The categories of this paper are: atom-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: chao-dyn
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The categories of this paper are: acc-phys
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The categories of this paper are: math
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The categories of this paper are: nucl-ex
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The categories of this paper are: quant-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: q-bio
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The categories of this paper are: adap-org