4 research outputs found
FPGA Energy Efficiency by Leveraging Thermal Margin
Cutting edge FPGAs are not energy efficient as conventionally presumed to be,
and therefore, aggressive power-saving techniques have become imperative. The
clock rate of an FPGA-mapped design is set based on worst-case conditions to
ensure reliable operation under all circumstances. This usually leaves a
considerable timing margin that can be exploited to reduce power consumption by
scaling voltage without lowering clock frequency. There are hurdles for such
opportunistic voltage scaling in FPGAs because (a) critical paths change with
designs, making timing evaluation difficult as voltage changes, (b) each FPGA
resource has particular power-delay trade-off with voltage, (c) data corruption
of configuration cells and memory blocks further hampers voltage scaling. In
this paper, we propose a systematical approach to leverage the available
thermal headroom of FPGA-mapped designs for power and energy improvement. By
comprehensively analyzing the timing and power consumption of FPGA building
blocks under varying temperatures and voltages, we propose a thermal-aware
voltage scaling flow that effectively utilizes the thermal margin to reduce
power consumption without degrading performance. We show the proposed flow can
be employed for energy optimization as well, whereby power consumption and
delay are compromised to accomplish the tasks with minimum energy. Lastly, we
propose a simulation framework to be able to examine the efficiency of the
proposed method for other applications that are inherently tolerant to a
certain amount of error, granting further power saving opportunity.
Experimental results over a set of industrial benchmarks indicate up to 36%
power reduction with the same performance, and 66% total energy saving when
energy is the optimization target.Comment: Accepted in IEEE International Conference on Computer Design (ICCD)
201
Towards Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions
The ever-increasing number of resource-constrained Machine-Type Communication
(MTC) devices is leading to the critical challenge of fulfilling diverse
communication requirements in dynamic and ultra-dense wireless environments.
Among different application scenarios that the upcoming 5G and beyond cellular
networks are expected to support, such as eMBB, mMTC and URLLC, mMTC brings the
unique technical challenge of supporting a huge number of MTC devices, which is
the main focus of this paper. The related challenges include QoS provisioning,
handling highly dynamic and sporadic MTC traffic, huge signalling overhead and
Radio Access Network (RAN) congestion. In this regard, this paper aims to
identify and analyze the involved technical issues, to review recent advances,
to highlight potential solutions and to propose new research directions. First,
starting with an overview of mMTC features and QoS provisioning issues, we
present the key enablers for mMTC in cellular networks. Along with the
highlights on the inefficiency of the legacy Random Access (RA) procedure in
the mMTC scenario, we then present the key features and channel access
mechanisms in the emerging cellular IoT standards, namely, LTE-M and NB-IoT.
Subsequently, we present a framework for the performance analysis of
transmission scheduling with the QoS support along with the issues involved in
short data packet transmission. Next, we provide a detailed overview of the
existing and emerging solutions towards addressing RAN congestion problem, and
then identify potential advantages, challenges and use cases for the
applications of emerging Machine Learning (ML) techniques in ultra-dense
cellular networks. Out of several ML techniques, we focus on the application of
low-complexity Q-learning approach in the mMTC scenarios. Finally, we discuss
some open research challenges and promising future research directions.Comment: 37 pages, 8 figures, 7 tables, submitted for a possible future
publication in IEEE Communications Surveys and Tutorial