in-memory computing analog

**In-Memory Computing (CIM)** is a **paradigm shift where multiply-accumulate (MAC) operations execute directly within memory arrays using analog charge accumulation, eliminating the von Neumann bottleneck of moving data between memory and processing units.** **Analog MAC in SRAM/RRAM Arrays** - **SRAM CIM**: Bit-cell current modulated by stored weight during read. Sense amplifier sums weighted currents across rows/columns. MAC result in analog domain (current/voltage). - **RRAM CIM**: Memristor conductance programs weight. Word line pulse applies activation voltage; output current proportional to activation × weight. - **Dot-Product Computation**: Column (or row) of weights simultaneously multiplied by single activation. N-way parallelism with single read operation vs N separate reads in traditional memory. **Weight-Stationary Architecture** - **Static Weights**: Weights stored permanently in memory cells (SRAM/RRAM). Single input activation stream processed against all weights. - **Output Stationary Alternative**: Weights stream, partial sums accumulate. Less common due to reduced memory locality. - **Systolic-like Operation**: Different from systolic arrays. Data flows to distributed memory, computation happens in-situ rather than in dedicated ALUs. **Peripheral Analog/Digital Conversion** - **Input DAC**: Converts digital activation to analog voltage/current for memory access. Must handle weight precision (6-8 bits typical). - **Output ADC**: Sense amplifier output integrates accumulated charge. Quantization noise limits precision. Typically 8-10 effective bits. - **Noise and Variability**: Semiconductor mismatch (Vth variation) and process/temperature drift degrade MAC accuracy. Requires statistical modeling and resilient algorithms. **Digital vs Analog CIM Trade-offs** - **Analog Advantages**: Energy efficiency (10-100x better per MAC), density (no multiplier area), single-cycle latency. - **Analog Disadvantages**: Noise sensitivity limits precision (quantization, thermal noise), requires accurate ADC/DAC, temperature compensation. - **Digital CIM Alternative**: Compute in digital domain within memory (bit-serial multiplication). Lower power than CPU/GPU but higher than analog CIM. **Die-Level Energy Comparison and Applications** - **Energy per MAC**: Analog CIM ~10-100 fJ/MAC. CPU/GPU ~1-10 pJ/MAC. 10-100x improvement for inference. - **Scalability Limits**: Analog CIM shines for matrix multiplication bottlenecks (DNNs, linear transformations). Doesn't help for sparse patterns or data-dependent control flow. - **Adoption Status**: Research phase in academia and DARPA MALIBU programs. Few commercial products (Samsung, Mythic AI developing). Requires compiler/framework support for practical deployment.