24 research outputs found
Coding for Phase Change Memory Performance Optimization
Over the past several decades, memory technologies have exploited
continual scaling of CMOS to drastically improve performance and
cost. Unfortunately, charge-based memories become unreliable beyond
20 nm feature sizes. A promising alternative is Phase-Change-Memory
(PCM) which leverages scalable resistive thermal mechanisms. To
realize PCM's potential, a number of challenges, including the
limited wear-endurance and costly writes, need to be addressed. This
thesis introduces novel methodologies for encoding data on PCM which exploit asymmetries in read/write performance to minimize memory's wear/energy consumption. First, we map the problem to a
distance-based graph clustering problem and prove it is NP-hard.
Next, we propose two different approaches: an optimal solution
based on Integer-Linear-Programming, and an approximately-optimal solution based on Dynamic-Programming. Our methods target both single-level and multi-level cell PCM and provide further
optimizations for stochastically-distributed data. We devise a low
overhead hardware architecture for the encoder. Evaluations
demonstrate significant performance gains of our framework
Hybrid heterogeneous energy supply networks
Abstract—Efficient energy supply, storage, and distribution are key technical challenges for design and operation of electronic systems. In particular, energy supply is the most scarce resource and constraint for mobile embedded systems where the supply lifetime, cost, weight, size, and portability are major concerns. With the advent of newer electrical energy supply and storage technologies, energy supplies with differing energy/power storage densities, cost, size, and recycling abilities are becoming available. Efficient system design requires an exact matching between the fluctuating load demands and the underlying energy resources. Combining the energy supplies in a hierarchical way creates a unique opportunity for efficient matching and variable load serving. Such a heterogeneous hybrid network of energy supply components could address a variety of power needs and serve a much broader range of system loads with a high efficiency. This paper presents a Hierarchial Architecture of Heterogenous Electrical Energy Supplies (HierArcHEES) that can be tuned to different load demands. I
Learning Scheduling Algorithms for Data Processing Clusters
Efficiently scheduling data processing jobs on distributed compute clusters
requires complex algorithms. Current systems, however, use simple generalized
heuristics and ignore workload characteristics, since developing and tuning a
scheduling policy for each workload is infeasible. In this paper, we show that
modern machine learning techniques can generate highly-efficient policies
automatically. Decima uses reinforcement learning (RL) and neural networks to
learn workload-specific scheduling algorithms without any human instruction
beyond a high-level objective such as minimizing average job completion time.
Off-the-shelf RL techniques, however, cannot handle the complexity and scale of
the scheduling problem. To build Decima, we had to develop new representations
for jobs' dependency graphs, design scalable RL models, and invent RL training
methods for dealing with continuous stochastic job arrivals. Our prototype
integration with Spark on a 25-node cluster shows that Decima improves the
average job completion time over hand-tuned scheduling heuristics by at least
21%, achieving up to 2x improvement during periods of high cluster load
Embroid: Unsupervised Prediction Smoothing Can Improve Few-Shot Classification
Recent work has shown that language models' (LMs) prompt-based learning
capabilities make them well suited for automating data labeling in domains
where manual annotation is expensive. The challenge is that while writing an
initial prompt is cheap, improving a prompt is costly -- practitioners often
require significant labeled data in order to evaluate the impact of prompt
modifications. Our work asks whether it is possible to improve prompt-based
learning without additional labeled data. We approach this problem by
attempting to modify the predictions of a prompt, rather than the prompt
itself. Our intuition is that accurate predictions should also be consistent:
samples which are similar under some feature representation should receive the
same prompt prediction. We propose Embroid, a method which computes multiple
representations of a dataset under different embedding functions, and uses the
consistency between the LM predictions for neighboring samples to identify
mispredictions. Embroid then uses these neighborhoods to create additional
predictions for each sample, and combines these predictions with a simple
latent variable graphical model in order to generate a final corrected
prediction. In addition to providing a theoretical analysis of Embroid, we
conduct a rigorous empirical evaluation across six different LMs and up to 95
different tasks. We find that (1) Embroid substantially improves performance
over original prompts (e.g., by an average of 7.3 points on GPT-JT), (2) also
realizes improvements for more sophisticated prompting strategies (e.g.,
chain-of-thought), and (3) can be specialized to domains like law through the
embedding functions.Comment: 38 pages, 22 figures, 8 table