4,972 research outputs found
Arbitrary Hardware/Software Trade Offs
This paper discusses a novel transformation-based design methodology and its use in the design of complex programmable VLSI systems. During the life-cycle of a complex system, the optimal trade-off between partially implementing in hardware or software is changing. This is due to varying system requirements (short time-to-market, low-cost, low-power, etc.) and improving the device technology. The proposed methodology allows such redesigns to be made using different hardware-software trade-offs, in a guaranteed correct wa
Optimizing I/O for Big Array Analytics
Big array analytics is becoming indispensable in answering important
scientific and business questions. Most analysis tasks consist of multiple
steps, each making one or multiple passes over the arrays to be analyzed and
generating intermediate results. In the big data setting, I/O optimization is a
key to efficient analytics. In this paper, we develop a framework and
techniques for capturing a broad range of analysis tasks expressible in
nested-loop forms, representing them in a declarative way, and optimizing their
I/O by identifying sharing opportunities. Experiment results show that our
optimizer is capable of finding execution plans that exploit nontrivial I/O
sharing opportunities with significant savings.Comment: VLDB201
Energy challenges for ICT
The energy consumption from the expanding use of information and communications technology (ICT) is unsustainable with present drivers, and it will impact heavily on the future climate change. However, ICT devices have the potential to contribute signi - cantly to the reduction of CO2 emission and enhance resource e ciency in other sectors, e.g., transportation (through intelligent transportation and advanced driver assistance systems and self-driving vehicles), heating (through smart building control), and manu- facturing (through digital automation based on smart autonomous sensors). To address the energy sustainability of ICT and capture the full potential of ICT in resource e - ciency, a multidisciplinary ICT-energy community needs to be brought together cover- ing devices, microarchitectures, ultra large-scale integration (ULSI), high-performance computing (HPC), energy harvesting, energy storage, system design, embedded sys- tems, e cient electronics, static analysis, and computation. In this chapter, we introduce challenges and opportunities in this emerging eld and a common framework to strive towards energy-sustainable ICT
BlendNet: Design and Optimization of a Neural Network-Based Inference Engine Blending Binary and Fixed-Point Convolutions
This paper presents BlendNet, a neural network architecture employing a novel
building block called Blend module, which relies on performing binary and
fixed-point convolutions in its main and skip paths, respectively. There is a
judicious deployment of batch normalizations on both main and skip paths inside
the Blend module and in between consecutive Blend modules. This paper also
presents a compiler for mapping various BlendNet models obtained by replacing
some blocks/modules in various vision neural network models with BlendNet
modules to FPGA devices with the goal of minimizing the end-to-end inference
latency while achieving high output accuracy. BlendNet-20, derived from
ResNet-20 trained on the CIFAR-10 dataset, achieves 88.0% classification
accuracy (0.8% higher than the state-of-the-art binary neural network) while it
only takes 0.38ms to process each image (1.4x faster than state-of-the-art).
Similarly, our BlendMixer model trained on the CIFAR-10 dataset achieves 90.6%
accuracy (1.59% less than full precision MLPMixer) while achieving a 3.5x
reduction in the model size. Moreover, The reconfigurability of DSP blocks for
performing 48-bit bitwise logic operations is utilized to achieve low-power
FPGA implementation. Our measurements show that the proposed implementation
yields 2.5x lower power consumption.Comment: 7 pages - under revie
Estimating Gibbs free energies via isobaric-isothermal flows
We present a machine-learning model based on normalizing flows that is
trained to sample from the isobaric-isothermal ensemble. In our approach, we
approximate the joint distribution of a fully-flexible triclinic simulation box
and particle coordinates to achieve a desired internal pressure. This novel
extension of flow-based sampling to the isobaric-isothermal ensemble yields
direct estimates of Gibbs free energies. We test our NPT-flow on monatomic
water in the cubic and hexagonal ice phases and find excellent agreement of
Gibbs free energies and other observables compared with established baselines.Comment: 19 pages, 7 figure
- …