360 research outputs found
Efficient DSP and Circuit Architectures for Massive MIMO: State-of-the-Art and Future Directions
Massive MIMO is a compelling wireless access concept that relies on the use
of an excess number of base-station antennas, relative to the number of active
terminals. This technology is a main component of 5G New Radio (NR) and
addresses all important requirements of future wireless standards: a great
capacity increase, the support of many simultaneous users, and improvement in
energy efficiency. Massive MIMO requires the simultaneous processing of signals
from many antenna chains, and computational operations on large matrices. The
complexity of the digital processing has been viewed as a fundamental obstacle
to the feasibility of Massive MIMO in the past. Recent advances on
system-algorithm-hardware co-design have led to extremely energy-efficient
implementations. These exploit opportunities in deeply-scaled silicon
technologies and perform partly distributed processing to cope with the
bottlenecks encountered in the interconnection of many signals. For example,
prototype ASIC implementations have demonstrated zero-forcing precoding in real
time at a 55 mW power consumption (20 MHz bandwidth, 128 antennas, multiplexing
of 8 terminals). Coarse and even error-prone digital processing in the antenna
paths permits a reduction of consumption with a factor of 2 to 5. This article
summarizes the fundamental technical contributions to efficient digital signal
processing for Massive MIMO. The opportunities and constraints on operating on
low-complexity RF and analog hardware chains are clarified. It illustrates how
terminals can benefit from improved energy efficiency. The status of technology
and real-life prototypes discussed. Open challenges and directions for future
research are suggested.Comment: submitted to IEEE transactions on signal processin
Cross-Layer Optimization for Power-Efficient and Robust Digital Circuits and Systems
With the increasing digital services demand, performance and power-efficiency
become vital requirements for digital circuits and systems. However, the
enabling CMOS technology scaling has been facing significant challenges of
device uncertainties, such as process, voltage, and temperature variations. To
ensure system reliability, worst-case corner assumptions are usually made in
each design level. However, the over-pessimistic worst-case margin leads to
unnecessary power waste and performance loss as high as 2.2x. Since
optimizations are traditionally confined to each specific level, those safe
margins can hardly be properly exploited.
To tackle the challenge, it is therefore advised in this Ph.D. thesis to
perform a cross-layer optimization for digital signal processing circuits and
systems, to achieve a global balance of power consumption and output quality.
To conclude, the traditional over-pessimistic worst-case approach leads to
huge power waste. In contrast, the adaptive voltage scaling approach saves
power (25% for the CORDIC application) by providing a just-needed supply
voltage. The power saving is maximized (46% for CORDIC) when a more aggressive
voltage over-scaling scheme is applied. These sparsely occurred circuit errors
produced by aggressive voltage over-scaling are mitigated by higher level error
resilient designs. For functions like FFT and CORDIC, smart error mitigation
schemes were proposed to enhance reliability (soft-errors and timing-errors,
respectively). Applications like Massive MIMO systems are robust against lower
level errors, thanks to the intrinsically redundant antennas. This property
makes it applicable to embrace digital hardware that trades quality for power
savings.Comment: 190 page
Low-Power and Error-Resilient VLSI Circuits and Systems.
Efficient low-power operation is critically important for the success of the next-generation signal processing applications. Device and supply voltage have been continuously scaled to meet a more constrained power envelope, but scaling has created resiliency challenges, including increasing timing faults and soft errors. Our research aims at designing low-power and robust circuits and systems for signal processing by drawing circuit, architecture, and algorithm approaches.
To gain an insight into the system faults due to supply voltage reduction, we researched the two primary effects that determine the minimum supply voltage (VMIN) in Intel’s tri-gate CMOS technology, namely process variations and gate-dielectric soft breakdown. We determined that voltage scaling increases the timing window that sequential circuits are vulnerable. Thus, we proposed a new hold-time violation metric to define hold-time VMIN, which has been adopted as a new design standard.
Device scaling increases soft errors which affect circuit reliability. Through extensive soft error characterization using two 65nm CMOS test chips, we studied the soft error mechanisms and its dependence on supply voltage and clock frequency. This study laid the foundation of the first 65nm DSP chip design for a NASA spaceflight project. To mitigate such random errors, we proposed a new confidence-driven architecture that effectively enhances the error resiliency of deeply scaled CMOS and post-CMOS circuits.
Designing low-power resilient systems can effectively leverage application-specific algorithmic approaches. To explore design opportunities in the algorithmic domain, we demonstrate an application-specific detection and decoding processor for multiple-input multiple-output (MIMO) wireless communication. To enhance the receive error rate for a robust wireless communication, we designed a joint detection and decoding technique by enclosing detection and decoding in an iterative loop to enhance both interference cancellation and error reduction. A proof-of-concept chip design was fabricated for the next-generation 4x4 256QAM MIMO systems. Through algorithm-architecture optimizations and low-power circuit techniques, our design achieves significant improvements in throughput, energy efficiency and error rate, paving the way for future developments in this area.PhDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/110323/1/uchchen_1.pd
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