###### Abstract  
The compute-in-memory (CIM) which embeds computation inside memory is an attractive scheme to circumvent von Neumann bottlenecks. This study proposes a logic-compatible embedded DRAM architecture that supports data storage as well as versatile digital computations. The proposed configurable memory unit operates in three modes: (1) memory mode in which it works as a normal dynamic memory, (2) logic-arithmetic mode where it performs bit-wise Boolean logic and full adder operations on two words stored within the memory array, and (3) convolution mode in which it executes digitally XNOR-and-accumulate (XAC) operation for binarized neural networks. A 1.0-V 4096-word \(\times\) 8-bit computational DRAM implemented in a 45-nanometer CMOS technology performs memory, logic and arithmetic operations at 241, 229, and 224 MHz while consuming the energy of 7.92, 8.09, and 8.19 pJ/cycle. Compared with conventional digital computing, it saves energy and latency of the arithmetic operation by at least 47% and 46%, respectively. For \(\mathrm{V_{DD}}=1.0\) V, the proposed CIM unit performs two 128-input XAC operations at 292 MHz with an energy consumption of 20.8 pJ/cycle, achieving 24.6 TOPS/W. This marks at least 11.9\(\times\) better energy efficiency and 38.8\(\times\) better delay, thereby achieving at least 461\(\times\) better energy-delay product than traditional 8-bit wide computing hardware. configurable embedded DRAM; logic operation; arithmetic operation; binary convolution; compute-in-memory +
Footnote †: journal: Applied  
computations directly on the memory bitlines because of its destructive readout. The CIM implementations based on nonvolatile memories such as RRAM [33; 34; 35; 36; 37; 38; 39; 40], PCRAM [41], STT-MRAM [42], or memristor [43] have also fostered computing in memory, but such emerging technologies are still immature. Poor reliability and the low endurance of RRAM devices [44], the long write time and high write energy of PCRAMs [45], the low on-off ratio of STT-MRAM cells [46], the bad scalability and limited yield of memristive devices [47], etc., make them less attractive for accurate and efficient CIM units. In particular, the embedded gain cell DRAM (GCDRAM) is one of the prospective candidates to realize the CIM scheme, owing to its compatibility with standard logic CMOS process and compact bit cell area. Despite the relatively short retention time of the embedded gain cell itself, the prior GCDRAMs [48; 49; 50; 51; 52; 53] achieved better area efficiency and much smaller standby power dissipation compared with 6T or read-decoupled (RD) 8T SRAMs. In addition, recent GCDRAM-based CIM works [54; 55; 56; 57; 58; 59; 60; 61] have demonstrated the multiply-and-average (MAV) or multiply-and-accumulate (MAC) computations within the gain cell array. They improved substantially both power consumption and throughput in processing the deep neural networks (DNNs). In this work, we propose another GCDRAM-based CIM unit, called the digital computing gain cell DRAM (DC-GCDRAM). The proposed DC-GCDRAM array supports three modes of operation: memory mode, logic-arithmetic mode, and convolution mode. In the memory mode, the DC-GCDRAM works as a usual DRAM compatible with standard CMOS technology. In the logic-arithmetic mode, the proposed architecture performs bit-wise Boolean logic as well as arithmetic computations on operands stored within the memory array. In the convolution mode, the proposed DC-GCDRAM executes the binary XNOR-and-accumulate (XAC) operation on weights stored within the cell array and external input activations. This full-digital CIM unit maintains high circuit reliability. It effectively eliminates the analog design issues such as data conversion overheads and computing accuracy loss caused by process variations. All of the results were obtained using 45-nanometer generic CMOS technology. The rest of this article is organized as follows. Section 2 briefly describes the basics of the memory cell used in this work. Section 3 details the proposed DC-GCDRAM architecture.