387 research outputs found
Formula of Entropy along Unstable Foliations for Diffeomorphisms with Dominated Splitting
Metric entropies along a hierarchy of unstable foliations are investigated
for diffeomorphisms with dominated splitting. The analogues of Ruelle's
inequality and Pesin's formula, which relate the metric entropy and Lyapunov
exponents in each hierarchy, are given
HAQ: Hardware-Aware Automated Quantization with Mixed Precision
Model quantization is a widely used technique to compress and accelerate deep
neural network (DNN) inference. Emergent DNN hardware accelerators begin to
support mixed precision (1-8 bits) to further improve the computation
efficiency, which raises a great challenge to find the optimal bitwidth for
each layer: it requires domain experts to explore the vast design space trading
off among accuracy, latency, energy, and model size, which is both
time-consuming and sub-optimal. Conventional quantization algorithm ignores the
different hardware architectures and quantizes all the layers in a uniform way.
In this paper, we introduce the Hardware-Aware Automated Quantization (HAQ)
framework which leverages the reinforcement learning to automatically determine
the quantization policy, and we take the hardware accelerator's feedback in the
design loop. Rather than relying on proxy signals such as FLOPs and model size,
we employ a hardware simulator to generate direct feedback signals (latency and
energy) to the RL agent. Compared with conventional methods, our framework is
fully automated and can specialize the quantization policy for different neural
network architectures and hardware architectures. Our framework effectively
reduced the latency by 1.4-1.95x and the energy consumption by 1.9x with
negligible loss of accuracy compared with the fixed bitwidth (8 bits)
quantization. Our framework reveals that the optimal policies on different
hardware architectures (i.e., edge and cloud architectures) under different
resource constraints (i.e., latency, energy and model size) are drastically
different. We interpreted the implication of different quantization policies,
which offer insights for both neural network architecture design and hardware
architecture design.Comment: CVPR 2019. The first three authors contributed equally to this work.
Project page: https://hanlab.mit.edu/projects/haq
Evaluating Performance Persistence in US Open-End Mutual Funds
Performance persistence in US open-end mutual funds is a contentious issue. This paper examines the performance persistence by analyzing monthly returns of mutual funds under nine investment styles over the periods of January 1993 to December 2008. We find that there is some evidence to support the persistence of mutual fund performance. Albeit this, a zero-investment best-minus-worst strategy does outperform the market with a certain level of consistency
Hardware-Centric AutoML for Mixed-Precision Quantization
Model quantization is a widely used technique to compress and accelerate deep
neural network (DNN) inference. Emergent DNN hardware accelerators begin to
support mixed precision (1-8 bits) to further improve the computation
efficiency, which raises a great challenge to find the optimal bitwidth for
each layer: it requires domain experts to explore the vast design space trading
off among accuracy, latency, energy, and model size, which is both
time-consuming and sub-optimal. Conventional quantization algorithm ignores the
different hardware architectures and quantizes all the layers in a uniform way.
In this paper, we introduce the Hardware-Aware Automated Quantization (HAQ)
framework which leverages the reinforcement learning to automatically determine
the quantization policy, and we take the hardware accelerator's feedback in the
design loop. Rather than relying on proxy signals such as FLOPs and model size,
we employ a hardware simulator to generate direct feedback signals (latency and
energy) to the RL agent. Compared with conventional methods, our framework is
fully automated and can specialize the quantization policy for different neural
network architectures and hardware architectures. Our framework effectively
reduced the latency by 1.4-1.95x and the energy consumption by 1.9x with
negligible loss of accuracy compared with the fixed bitwidth (8 bits)
quantization. Our framework reveals that the optimal policies on different
hardware architectures (i.e., edge and cloud architectures) under different
resource constraints (i.e., latency, energy, and model size) are drastically
different. We interpreted the implication of different quantization policies,
which offer insights for both neural network architecture design and hardware
architecture design.Comment: Journal preprint of arXiv:1811.08886 (IJCV, 2020). The first three
authors contributed equally to this work. Project page:
https://hanlab.mit.edu/projects/haq
- …