Associative property

Macro

Pure mathematics

Computer network

Mathematics

Latency (audio)

Encryption

Artificial intelligence

Programming language

Computer science

Computer hardware

Artificial neural network

Content-addressable memory

Telecommunications

Nonvolatile computing-in-memory (nvCIM) [1]–[4] is ideal for battery-powered tiny artificial intelligence (AI) edge devices that require nonvolatile data storage and low system-level power consumption. Data encryption/decryption (data-ED) is also required to prevent access to the neural network (NN) model weights and the personalized data used to improve inference accuracy. This paper presents an AI nvCIM data-ED-capable macro with high energy efficiency (EF <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MAC</inf> ), a low macro-level read latency (t <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AC-M</inf> ), a high read bandwidth (R-BW), and high-precision inputs (IN), weights (W), and outputs (OUT) for multiply-and-accumulate (MAC) operations. Prior nvCIM macros designed for MAC operations [1]–[3] do not support data-ED or a high number of accumulations (ACU). The use of a single NN layer also requires multiple cycles for full-channel MAC (MAC <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FC-L</inf> operations. A low computing latency (t <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AC-FC-L</inf> ) and high-precision nvCIM macro with data-ED design faces the following challenges: (1) long t <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AC-FC-L</inf> and low EF <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MAC</inf> for MAC <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FC-L</inf> operations, which requires multiple memory accesses with a limited R-BW; (2) long t <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AC-M</inf> due to BL pre-charge (t <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PRE</inf> ), signal development (t <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SD</inf> ), sensing (t <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SA</inf> ), and data-D (t <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OE</inf> ); (3) High power consumption for BL precharge, particularly when using a high BL read voltage (V <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RD</inf> ) to increase sensing yield.

A 22nm 4Mb STT-MRAM Data-Encrypted Near-Memory Computation Macro with a 192GB/s Read-and-Decryption Bandwidth and 25.1-55.1TOPS/W 8b MAC for AI Operations