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Near Memory Processing

  • NMP: Near Memory Processing
  • NDC: Near Data Computing

  • PRIME: A Novel Processing-in-memory Architecture for Neural NetworkComputation in ReRAM-based Main Memory, ISCA'16

    • High-performance acceleration of NN requires high memory bandwidth since the PUs are hungry for fetching the synaptic weights [17]. To address this challenge, recent special-purpose chip designs have adopted large on-chip memory to store the synaptic weights. For example, DaDianNao [18] employed a large on-chip eDRAM for both high bandwidth and data locality; TrueNorth utilized an SRAM crossbar memory for synapses in each core [19].
  • DianNao and DaDianNao
    • memory bandwidth requirements of two important layer types: convolutional layers with private kernels (used in DNNs) and classifier layers used in both CNNs and DNNs. For these types of layers, the total number of required synapses can be massive, in the millions of parameters, or even tens or hundreds thereof.
    • providing sufficient eDRAM capacity to hold all synapse on the combined eDRAM of all chips will save on off-chip DRAM accesses, which are particularly costly energy-wise
    • Synapses. In a perceptron layer, all synapses are usually unique, and thus there is no reuse within the layer. On the other hand, the synapses are reused across network invocations, i.e., for each new input data (also called “input row”) presented to the neural network. So a sufficiently large L2 could store all network synapses and take advantage of that locality. For DNNs with private kernels, this is not possible as the total number of synapses are in the tens or hundreds of millions (the largest network to date has a billion synapses [26]). However, for both CNNs and DNNs with shared kernels, the total number of synapses range in the millions, which is within the reach of an L2 cache. In Figure 6, see CLASS1 - Tiled+L2, we emulate the case where reuse across network invocations is possible by considering only the perceptron layer; as a result, the total bandwidth requirements are now drastically reduced.
    • So, ML workloads do need large memory bandwidth, and need a lot memory. But how about temporary working set size? It’s the best if it has a reasonable working set size that can fit the cache.
  • TPU
    • Each model needs between 5M and 100M weights (9th column of Table 1), which can take a lot of time and energy to access. To amortize the access costs, the same weights are reused across a batch of independent examples during inference or training, which improves performance.
    • The weights for the matrix unit are staged through an onchip Weight FIFO that reads from an off-chip 8 GiB DRAM called Weight Memory (for inference, weights are read-only; 8 GiB supports many simultaneously active models). The weight FIFO is four tiles deep. The intermediate results are held in the 24 MiB on-chip Unified Buffer, which can serve as inputs to the Matrix Unit.
    • In virtual cache model, we actually can assign those weights to some designated sets, thus avoid conflicting with other data, which means we can sustain those weights in cache!

To conclude:
a) ML needs to use weight/synapses during computation, and those data will be reused repeatly across different stages. Besides, output from last stage serves the input of next stage, so buffering the intermediate data is important. Most ML accelerators use some kind of on-chip memory (Weighted FIFO, Unified Cache in TPU) to buffer those data. This fits the HBM+Disaggregated Memory model: HBM is the on-chip memory, while disaggregated memory is the off-chip memory. b) Combined with virtual cache, we could assign special virtual addresses to weight data, so they stay in some designated cache sets. Kernel can avoid allocating conflict virtual addresses later. Thus we can retain these weight data in virtual cache easily.