Compute In Memory Diagram
Figure 3 The block diagram of architecture template file used to represent CiM primitives in Timeloop. Memory level is re-purposed into compute, represented as a network of multiple CiM arrays. Each CiM array consists of individual CiM units which can operate in parallel and each CiM unit computes one multiply-accumulate MAC operation at a time.
The compute-in-memory circuitry of the memory may perform an arithmetic operation using the first set of data from the programmable logic circuitry and a second set of data stored in the memory. FIG. 10 is a block diagram of compute-in-memory circuitry of the base die that may be used for tensor computations,
This memory is operating as register memory for the actual compute engine, so a CNM structure is a more optimized von Neumann structure with memory close by. The
The block diagram of TPU v1 Figure 5 clearly illustrates the near-memory architecture where the compute and memory blocks are physically separate. The concept is extended to subsequent generations where external HBM memory is used for storing data with scaler and vector units having limited internal SRAM memory.
The recent compute-in-memory CiM architectures are proposed as a promising solution to support Deep Neural Network and Convolutional Neural Network to solve large and complex tasks in various
A four-megabit compute-in-memory macro with eight-bit precision based on CMOS and resistive random-access memory for AI edge devices. Nat. Electron. 4 , 921-930 2021.
One of the main assets of the human brain which enables low energy consumption is its peculiar architecture, where memory and computation are colocated. 6 This is against the conventional computer architecture, where computing takes place in a central processing unit CPU according to programs and data which are fetched from a working memory
Figure 27 a Block diagram of transpose SRAM sub-array and periphery circuity. b RRAM based ADC-free in-memory compute scheme validated with a prototype chip in TSMC 40nm process, which can significantly improve the hardware performance over the conventional CIM designs while achieving
near memory computation still digital eliminates data transfer costs memory read energy dominates deep in-memory memory access and computation combined mixed signal computation significant energy amp latency reduction SRAM Bank Memory ALU Digital processing SRAM Bank Digital processing SRAM Bank Mixed signal Processing
Because of the merged data storage and computing units, compute-in-memory is becoming one of the desirable choices for data-centric applications to mitigate the memory wall bottleneck in von-Neumann architecture. In this chapter, the recent architectural designs and underlying circuitdevice technologies for compute-in-memory are surveyed.
