7 research outputs found
Efficient Multiuser AI Downloading via Reusable Knowledge Broadcasting
For the 6G mobile networks, in-situ model downloading has emerged as an
important use case to enable real-time adaptive artificial intelligence on edge
devices. However, the simultaneous downloading of diverse and high-dimensional
models to multiple devices over wireless links presents a significant
communication bottleneck. To overcome the bottleneck, we propose the framework
of model broadcasting and assembling (MBA), which represents the first attempt
on leveraging reusable knowledge, referring to shared parameters among tasks,
to enable parameter broadcasting to reduce communication overhead. The MBA
framework comprises two key components. The first, the MBA protocol, defines
the system operations including parameter selection from a model library, power
control for broadcasting, and model assembling at devices. The second component
is the joint design of parameter-selection-and-power-control (PS-PC), which
provides guarantees on devices' model performance and minimizes the downloading
latency. The corresponding optimization problem is simplified by decomposition
into the sequential PS and PC sub-problems without compromising its optimality.
The PS sub-problem is solved efficiently by designing two efficient algorithms.
On one hand, the low-complexity algorithm of greedy parameter selection
features the construction of candidate model sets and a selection metric, both
of which are designed under the criterion of maximum reusable knowledge among
tasks. On the other hand, the optimal tree-search algorithm gains its
efficiency via the proposed construction of a compact binary tree pruned using
model architecture constraints and an intelligent branch-and-bound search.
Given optimal PS, the optimal PC policy is derived in closed form. Extensive
experiments demonstrate the substantial reduction in downloading latency
achieved by the proposed MBA compared to traditional model downloading.Comment: Submitted to IEEE for possible publicatio
Realizing In-Memory Baseband Processing for Ultra-Fast and Energy-Efficient 6G
To support emerging applications ranging from holographic communications to
extended reality, next-generation mobile wireless communication systems require
ultra-fast and energy-efficient baseband processors. Traditional complementary
metal-oxide-semiconductor (CMOS)-based baseband processors face two challenges
in transistor scaling and the von Neumann bottleneck. To address these
challenges, in-memory computing-based baseband processors using resistive
random-access memory (RRAM) present an attractive solution. In this paper, we
propose and demonstrate RRAM-implemented in-memory baseband processing for the
widely adopted multiple-input-multiple-output orthogonal frequency division
multiplexing (MIMO-OFDM) air interface. Its key feature is to execute the key
operations, including discrete Fourier transform (DFT) and MIMO detection using
linear minimum mean square error (L-MMSE) and zero forcing (ZF), in one-step.
In addition, RRAM-based channel estimation module is proposed and discussed. By
prototyping and simulations, we demonstrate the feasibility of RRAM-based
full-fledged communication system in hardware, and reveal it can outperform
state-of-the-art baseband processors with a gain of 91.2 in latency and
671 in energy efficiency by large-scale simulations. Our results pave a
potential pathway for RRAM-based in-memory computing to be implemented in the
era of the sixth generation (6G) mobile communications.Comment: arXiv admin note: text overlap with arXiv:2205.0356
Realizing Ultra-Fast and Energy-Efficient Baseband Processing Using Analogue Resistive Switching Memory
To support emerging applications ranging from holographic communications to
extended reality, next-generation mobile wireless communication systems require
ultra-fast and energy-efficient (UFEE) baseband processors. Traditional
complementary metal-oxide-semiconductor (CMOS)-based baseband processors face
two challenges in transistor scaling and the von Neumann bottleneck. To address
these challenges, in-memory computing-based baseband processors using resistive
random-access memory (RRAM) present an attractive solution. In this paper, we
propose and demonstrate RRAM-based in-memory baseband processing for the widely
adopted multiple-input-multiple-output orthogonal frequency division
multiplexing (MIMO-OFDM) air interface. Its key feature is to execute the key
operations, including discrete Fourier transform (DFT) and MIMO detection using
linear minimum mean square error (L-MMSE) and zero forcing (ZF), in one-step.
In addition, RRAM-based channel estimation as well as mapper/demapper modules
are proposed. By prototyping and simulations, we demonstrate that the
RRAM-based full-fledged communication system can significantly outperform its
CMOS-based counterpart in terms of speed and energy efficiency by and
times, respectively. The results pave a potential pathway for RRAM-based
in-memory computing to be implemented in the era of the sixth generation (6G)
mobile communications