Kang et al., 2018 - Google Patents

A 19.4-nJ/decision, 364-K decisions/s, in-memory random forest multi-class inference accelerator

Kang et al., 2018

Document ID
1263973610709075154
Author
Kang M
Gonugondla S
Lim S
Shanbhag N
Publication year
Publication venue
IEEE Journal of Solid-State Circuits

External Links

Snippet

This paper presents an integrated circuit (IC) realization of a random forest (RF) machine learning classifier in a 65-nm CMOS. Algorithm, architecture, and circuits are co-optimized to achieve aggressive energy and delay benefits by taking advantage of the inherent error …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/06Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50Computer-aided design
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F7/00Methods or arrangements for processing data by operating upon the order or content of the data handled
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G11INFORMATION STORAGE
    • G11CSTATIC STORES
    • G11C11/00Digital stores characterised by the use of particular electric or magnetic storage elements; Storage elements therefor
    • G11C11/21Digital stores characterised by the use of particular electric or magnetic storage elements; Storage elements therefor using electric elements
    • G11C11/34Digital stores characterised by the use of particular electric or magnetic storage elements; Storage elements therefor using electric elements using semiconductor devices
    • G11C11/40Digital stores characterised by the use of particular electric or magnetic storage elements; Storage elements therefor using electric elements using semiconductor devices using transistors
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F15/00Digital computers in general; Data processing equipment in general
    • GPHYSICS
    • G11INFORMATION STORAGE
    • G11CSTATIC STORES
    • G11C15/00Digital stores in which information comprising one or more characteristic parts is written into the store and in which information is read-out by searching for one or more of these characteristic parts, i.e. associative or content-addressed stores
    • G11C15/04Digital stores in which information comprising one or more characteristic parts is written into the store and in which information is read-out by searching for one or more of these characteristic parts, i.e. associative or content-addressed stores using semiconductor elements
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6268Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches

Similar Documents

Publication Publication Date Title
Kang et al. A 19.4-nJ/decision, 364-K decisions/s, in-memory random forest multi-class inference accelerator
Agrawal et al. Xcel-RAM: Accelerating binary neural networks in high-throughput SRAM compute arrays
Long et al. ReRAM-based processing-in-memory architecture for recurrent neural network acceleration
Kang et al. An in-memory VLSI architecture for convolutional neural networks
Valavi et al. A 64-tile 2.4-Mb in-memory-computing CNN accelerator employing charge-domain compute
Verma et al. In-memory computing: Advances and prospects
Si et al. A local computing cell and 6T SRAM-based computing-in-memory macro with 8-b MAC operation for edge AI chips
Marinella et al. Multiscale co-design analysis of energy, latency, area, and accuracy of a ReRAM analog neural training accelerator
Daniels et al. Energy-efficient stochastic computing with superparamagnetic tunnel junctions
Yin et al. Vesti: Energy-efficient in-memory computing accelerator for deep neural networks
Giacomin et al. A robust digital RRAM-based convolutional block for low-power image processing and learning applications
Kang et al. A 19.4 nJ/decision 364K decisions/s in-memory random forest classifier in 6T SRAM array
Kang et al. Deep in-memory architectures in SRAM: An analog approach to approximate computing
Sim et al. Scalable stochastic-computing accelerator for convolutional neural networks
Su et al. Two-way transpose multibit 6T SRAM computing-in-memory macro for inference-training AI edge chips
Kang et al. In-memory computing architectures for sparse distributed memory
Dbouk et al. A 0.44-μJ/dec, 39.9-μs/dec, recurrent attention in-memory processor for keyword spotting
Angizi et al. Pisa: A binary-weight processing-in-sensor accelerator for edge image processing
Chakraborty et al. Input-aware flow-based computing on memristor crossbars with applications to edge detection
Bose et al. A 51.3-TOPS/W, 134.4-GOPS in-memory binary image filtering in 65-nm CMOS
Kosuge et al. A 16 nJ/classification FPGA-based wired-logic DNN accelerator using fixed-weight non-linear neural net
Eslami et al. A flexible and reliable RRAM-based in-memory computing architecture for data-intensive applications
Zhao et al. Configurable memory with a multilevel shared structure enabling in-memory computing
Zhang et al. A Novel 9T1C-SRAM Compute-In-Memory Macro With Count-Less Pulse-Width Modulation Input and ADC-Less Charge-Integration-Count Output
Bae et al. CTLE-Ising: A Continuous-Time Latch-Based Ising Machine Featuring One-Shot Fully Parallel Spin Updates and Equalization of Spin States