IPCore
Mobilenet-SSD
Allocation for Memory-Computing Resources
Support for Inner Memory
Yes
Depth:Adjustable(32bit MAX)
Width:Auto-adjustible(128~512bit)
MAC Array Size
INT8
INT16
FP16
FP32
User Defined Bit Width
≥ 64x64
Supported Data Types
Conv
Pooling
ReLU
Concat
Supported Operator Types
Max pooling
Pooling Types
2^N (512bit MAX)
Corresponding Model’s Typical Latency @200MHz
1.71ms
Inception-v2
ResNet50
BERT
Capability of High-Precision Quantifying
Yes
Yes
Depth:Adjustable(32bit MAX)
Width:Auto-adjustible(128~512bit)
INT8
INT16
FP16
FP32
Max,Ave /3x3 5x5 7x7 9x9
2^N (512bit MAX)
≥ 64x64
Conv
Pooling
ReLU
Concat
BatchNorm
15.52ms
Yes
Yes
Depth:Adjustable(32bit MAX)
Width:Auto-adjustible(128~512bit)
INT8
INT16
FP16
FP32
Max,Ave /3x3 5x5 7x7 9x9
2^N (512bit MAX)
≥ 64x64
Conv
Pooling
ReLU
Concat
Scale
Yes
Depth:Adjustable(32bit MAX)
Width:Auto-adjustible(128~512bit)
INT8
INT16
FP16
FP32
2^N (512bit MAX)
≥ 64x64
Matrix
Embedding
Linear
LayerNorm
17.1ms
Yes
32.79ms
Yes
Product Parameters
Supporting multiple data types with high quantification accuracy
Computing in Memory with low latency and high energy efficiency ratio
Adjustable parameters for more flexible deployment
Reconfigurable architecture, supporting a wide variety of operators, with strong model compatibility
▪ Fully-connected layers
▪ Dilated convolutions
▪ Max pooling, Average Pooling
▪ ReLU, GeLU
▪ Matrix Multiplications
▪ Adjustable bit width
▪ Adjustable computing MAC resources
▪ Adjustable memory resources
▪ INT8, INT16, FP16, FP3
▪ Single model supports different precision quantization for each layer
▪ Single model supports different precision quantization for each layer
▪ High power density and high energy efficiency ratio
▪ Low external memory access and low latency
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