University of Michigan

Intel Corporation

Thomas Chen

Zhengya Zhang

Raghavan Kumar

Gregory Kengho Chen

Jae-sun Seo

Chester Liu

Jacob Christopher Botimer

Ching-En Lee

Shiming Song

Many signal processing, machine learning and scientific computing applications require a large number of multiply-accumulate (MAC) operations. This type of operation is demanding in both computation and memory. Process in memory has been proposed as a new technique that computes directly on a large array of data in place, to eliminate expensive data movement overhead. To enable parallel multi-bit MAC operations, both width- and level-modulating memory word lines are applied. To improve performance and provide tolerance against process-voltage-temperature variations, a delay-locked loop is used to generate fine unit pulses for driving memory word lines and a dual-ramp Single-slope ADC is used to convert bit line outputs. The concept is prototyped in a 180 nm CMOS test chip made of four 320&#xd7;64 compute-SRAMs, each supporting 128&#xd7; parallel 5 b&#xd7;5 b MACs with 32 5 b output ADCs and consuming 16.6 mW at 200 MHz.

SRAM-based process in memory system

A sparse video inference chip is designed to extract spatio-temporal features from videos for action classification and motion tracking. The core is a sparse video inference processor that implements recurrent neural network in three layers of processing. High sparsity is enforced in each layer of processing, reducing the complexity by two orders of magnitude and allowing all multiply-accumulates (MAC) to be replaced by select-accumulates (SA). The design is demonstrated in a 3.98 mm2 40 nm CMOS chip with an Open-RISC processor providing software-defined control and classification.

Sparse video inference processor for action classification and motion tracking

An apparatus and method are described for a neuromorphic processor design in which neuron timing information is duplicated on a neuromorphic core. For example, one embodiment of an apparatus comprises: a first neurosynaptic core comprising a plurality of neurons and a synapse array comprising a plurality of synapses to communicatively couple the plurality of neurons, each synapse connecting two neurons having a weight associated therewith, wherein a first neuron is to generate an output spike based on the weights of synapses over which inputs are received from the other neurons; a second neurosynaptic core also comprising a plurality of neurons and having at least one counter to maintain a count value indicative of spike timing for a second neuron, wherein a spike output of the second neuron in the second neurosynaptic core is communicatively coupled over a first synapse to the first neuron in the first neurosynaptic core; and a duplicate counter maintained within the first neurosynaptic core and synchronized with the counter from the second neurosynaptic core, the first neuron to use a first value from the duplicate counter to adjust the weight of the first synapse coupling the second neuron to the first neuron.

Apparatus and method for a digital neuromorphic processor

Growing community of inventors

Ann Arbor, MI, United States of America

Thomas Chen