文章目录
文献摘自A 9.02mW CNN-Stereo-Based Real-Time 3D Hand-Gesture Recognition Processor for Smart Mobile Devices
1 英文缩写
HGR: hand-gesture recognition手势检测
HMD: head-mounted displays头戴式设备
ToF: time-of-flight
PE: processing element
NNS: nearest-neighbor searching
PIM: processing-in-memory
CSE: CNN-stereo(立体) engine
ICP-PSO: iterative-closest-point/particle-swarm optimization-based（迭代最近点、粒子群优化）
IPE: ICP-PSO engine
FWD: forwarding
BWD: backwarding
2 overall architecture
In this paper, we describe an accurate, low power (<10mW), and real-time 3D HGR processor for smart mobile devices.
feature：
- a piplined CNN processing element with a shift MAC operation
- triple ping-pong buffers with workload balancing
- nearest-neighbor searching (NNS) processing-in-memory (PIM) for high energy efficiency
CNN-stereo engine(CSE)
- two line-streaming CNN cores
- 4 locally distributed memories
- 1 matching core
the CNN core
- 1 pipelined CNN PE
- a local DMA
- a forwarding/backwarding unit
ICP-PSE engine(IPE)
- a NNS unit with 16-way parallel NNS PIMs
- a hand-tracking unit
3 pipelined CNN PE architecture
The shift MAC operation with a 3×3 filter in consists of three stages
- shifting feature maps and filters
- element-wise multiplication
- partia-sum accumulation
The line-streaming CNN operation is accelerated by the 7-stage pipelined CNN PE that processes 48 MACs per cycle with 96% core utilization
4 triple ping-pong memories
The hardware utilizes triple ping-pong memories to store feature maps, where each memory is accessed simultaneously to feed pipeline inputs, write back pipeline outputs, and to access an external interface, respectively.
为什么是3？
Instead of storing the entire feature maps on the chip, the line-streaming processing with only 3-to-5 lines of feature maps reduces 90.1% of required data that must be fetched from/to off-chip。
如何 balance workload?
The FWD/BWD units keep CNN core workloads identical throughout CNN processing and exchange feature-map boundary data with one another when local feature maps are fetched.