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A Scalable Bayesian Inference Accelerator for Unsupervised Learning

Conference Paper

This article consists only of a collection of slides from the author's conference presentation.

Algorithm-Hardware Co-Design of Adaptive Floating-Point Encodings for Resilient Deep Learning Inference

Conference Paper

Conventional hardware-friendly quantization methods, such asfixed-point or integer, tend to perform poorly at very low preci-sion as their shrunken dynamic ranges cannot adequately capturethe wide data distributions commonly seen in sequence transduc-tion...

DeepRecSys: A System for Optimizing End-To-End At-scale Neural Recommendation Inference

Conference Paper

Neural personalized recommendation is the corner-stone of a wide collection of cloud services and products, constituting significant compute demand of the cloud infrastructure. Thus, improving the execution efficiency of neural recommendation directly...

RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing

Conference Paper

Personalized recommendation systems leverage deep learning models and account for the majority of data center AI cycles. Their performance is dominated by memory-bound sparse embedding operations with unique irregular memory access patterns that pose a...

A 3mm2 Programmable Bayesian Inference Accelerator for Unsupervised Machine Perception using Parallel Gibbs Sampling in 16nm

Conference Paper

This paper describes a 16nm programmable accelerator for unsupervised probabilistic machine perception tasks that performs Bayesian inference on probabilistic models mapped onto a 2D Markov Random Field, using MCMC. Exploiting two degrees of parallelism...

A Systematic Methodology for Analysis of Deep Learning Hardware and Software Platforms

Conference Paper

Training deep learning models is compute-intensive and there is an industry-wide trend towards hardware and software specialization to improve performance. To systematically compare deep learning systems, we introduce a methodology comprised of a set of...

The Architectural Implications of Facebook's DNN-based Personalized Recommendation

Conference Paper

The widespread application of deep learning has changed the landscape of computation in the data center. In particular, personalized recommendation for content ranking is now largely accomplished leveraging deep neural networks. However, despite the...

CHIPKIT: An agile, reusable open-source framework for rapid test chip development

Journal Article

The current trend for domain-specific architectures (DSAs) has led to renewed interest in research test chips to demonstrate new specialized hardware. Tape-outs also offer huge pedagogical value garnered from real hands-on exposure to the whole system...

CHAMPVis: Comparative Hierarchical Analysis of Microarchitectural Performance

Miscellaneous

Performance analysis and optimization are essential tasks for hardware and software engineers. In the age of datacenter-scale computing, it is particularly important to conduct comparative performance analysis to understand discrepancies and limitations...

MaxNVM: Maximizing DNN Storage Density and Inference Efficiency with Sparse Encoding and Error Mitigation

Conference Paper

Deeply embedded applications require low-power, low-cost hardware that fits within stringent area constraints. Deep learning has many potential uses in these domains, but introduces significant inefficiencies stemming from off-chip DRAM accesses of model...

MEMTI: Optimizing On-Chip Nonvolatile Storage for Visual Multitask Inference at the Edge

Journal Article

The combination of specialized hardware and embedded nonvolatile memories (eNVM) holds promise for energy-efficient deep neural network (DNN) inference at the edge. However, integrating DNN hardware accelerators with eNVMs still presents several...

MASR: A Modular Accelerator for Sparse RNNs

Conference Paper

Recurrent neural networks (RNNs) are becoming the de facto solution for speech recognition. RNNs exploit long-term temporal relationships in data by applying repeated, learned transformations. Unlike fully-connected (FC) layers with single vector matrix...