6 research outputs found
Intelligent systems for efficiency and security
As computing becomes ubiquitous and personalized, resources like energy, storage and time are becoming increasingly scarce and, at the same time, computing systems must deliver in multiple dimensions, such as high performance, quality of service, reliability, security and low power. Building such computers is hard, particularly when the operating environment is becoming more dynamic, and systems are becoming heterogeneous and distributed.
Unfortunately, computers today manage resources with many ad hoc heuristics that are suboptimal, unsafe, and cannot be composed across the computer’s subsystems. Continuing this approach has severe consequences: underperforming systems, resource waste, information loss, and even life endangerment.
This dissertation research develops computing systems which, through intelligent adaptation, deliver efficiency along multiple dimensions. The key idea is to manage computers with principled methods from formal control. It is with these methods that the multiple subsystems of a computer sense their environment and configure themselves to meet system-wide goals.
To achieve the goal of intelligent systems, this dissertation makes a series of contributions, each building on the previous. First, it introduces the use of formal MIMO (Multiple Input Multiple Output) control for processors, to simultaneously optimize many goals like performance, power, and temperature. Second, it develops the Yukta control system, which uses coordinated formal controllers in different layers of the stack (hardware and operating system). Third, it uses robust control to develop a fast, globally coordinated and decentralized control framework called Tangram, for heterogeneous computers. Finally, it presents Maya, a defense against power side-channel attacks that uses formal control to reshape the power dissipated by a computer, confusing the attacker. The ideas in the dissertation have been demonstrated successfully with several prototypes, including one built along with AMD (Advanced Micro Devices, Inc.) engineers. These designs significantly outperformed the state of the art.
The research in this dissertation brought formal control closer to computer architecture and has been well-received in both domains. It has the first application of full-fledged MIMO control for processors, the first use of robust control in computer systems, and the first application of formal control for side-channel defense. It makes a significant stride towards intelligent systems that are efficient, secure and reliable
Mitigating Catastrophic Forgetting in Long Short-Term Memory Networks
Continual learning on sequential data is critical for many machine learning
(ML) deployments. Unfortunately, LSTM networks, which are commonly used to
learn on sequential data, suffer from catastrophic forgetting and are limited
in their ability to learn multiple tasks continually. We discover that
catastrophic forgetting in LSTM networks can be overcome in two novel and
readily-implementable ways -- separating the LSTM memory either for each task
or for each target label. Our approach eschews the need for explicit
regularization, hypernetworks, and other complex methods. We quantify the
benefits of our approach on recently-proposed LSTM networks for computer memory
access prefetching, an important sequential learning problem in ML-based
computer system optimization. Compared to state-of-the-art weight
regularization methods to mitigate catastrophic forgetting, our approach is
simple, effective, and enables faster learning. We also show that our proposal
enables the use of small, non-regularized LSTM networks for complex natural
language processing in the offline learning scenario, which was previously
considered difficult
Defensive ML: Defending Architectural Side-channels with Adversarial Obfuscation
Side-channel attacks that use machine learning (ML) for signal analysis have
become prominent threats to computer security, as ML models easily find
patterns in signals. To address this problem, this paper explores using
Adversarial Machine Learning (AML) methods as a defense at the computer
architecture layer to obfuscate side channels. We call this approach Defensive
ML, and the generator to obfuscate signals, defender. Defensive ML is a
workflow to design, implement, train, and deploy defenders for different
environments. First, we design a defender architecture given the physical
characteristics and hardware constraints of the side-channel. Next, we use our
DefenderGAN structure to train the defender. Finally, we apply defensive ML to
thwart two side-channel attacks: one based on memory contention and the other
on application power. The former uses a hardware defender with ns-level
response time that attains a high level of security with half the performance
impact of a traditional scheme; the latter uses a software defender with
ms-level response time that provides better security than a traditional scheme
with only 70% of its power overhead.Comment: Preprint. Under revie
Quantum Cognitive Modeling: New Applications and Systems Research Directions
Expanding the benefits of quantum computing to new domains remains a
challenging task. Quantum applications are concentrated in only a few domains,
and driven by these few, the quantum stack is limited in supporting the
development or execution demands of new applications. In this work, we address
this problem by identifying both a new application domain, and new directions
to shape the quantum stack. We introduce computational cognitive models as a
new class of quantum applications. Such models have been crucial in
understanding and replicating human intelligence, and our work connects them
with quantum computing for the first time. Next, we analyze these applications
to make the case for redesigning the quantum stack for programmability and
better performance. Among the research opportunities we uncover, we study two
simple ideas of quantum cloud scheduling using data from gate-based and
annealing-based quantum computers. On the respective systems, these ideas can
enable parallel execution, and improve throughput. Our work is a contribution
towards realizing versatile quantum systems that can broaden the impact of
quantum computing on science and society
Systematically controlling the error rates in variation-prone networks-on-chip for energy efficiency
Networks-on-Chip (NoCs) are prone to within-die process variation as they
span the whole chip. To tolerate variation, their voltages (Vdd) carry overprovisioned
guardbands. As a result, prior work has proposed to save energy
by dynamically managing Vdd, operating at reduced Vdd while occasionally
su ering and xing errors. Unfortunately, these proposals use ad-hoc controller
designs that may not work under other scenarios and do not provide
error bounds.
This thesis develops a scheme that dynamically minimizes the Vdd of groups
of routers in a variation-prone NoC using formal control-theory methods.
The scheme, called Contra, saves substantial energy while guaranteeing the
stability and convergence of error rates. Moreover, the scheme is enhanced
with a low-cost secondary network that retransmits erroneous packets for
higher energy e ciency. The enhanced scheme is called Contra+. Both
Contra and Contra+ are evaluated using simulations of NoCs with 64{100
routers. In an NoC with 8 routers per Vdd domain, the proposed schemes
reduce the average energy consumption of the NoC by 27%; in a futuristic
NoC with one router per Vdd domain, Contra+ and Contra reduce the average
energy consumption by 37% and 32%, respectively. The performance impact
is negligible. These savings are signi cant over the state-of-the-art. The
results categorically state that formal control is essential to attain a stable,
scalable, and energy-efficient design. Additionally, it is found that while the
secondary network helps Contra+ attain higher energy savings, it has a nonnegligible
hardware cost. Hence, Contra is the most cost-effective design