Architecture Deep Dive
Explore the cutting-edge silicon innovations powering next-generation AI workloads
Core Architecture
Compute Clusters
128 Tensor Cores: 4096 MAC units per core @ 2.5 GHz
64 Vector Units: 256-bit SIMD with AVX-512 extensions
32 Scalar Processors: RISC-V RV64GC for control flow
Memory Subsystem
L1 Cache: 256 KB per cluster with 32-way associativity
L2 Cache: 32 MB shared, 2 TB/s internal bandwidth
HBM3 DRAM: 96 GB @ 3.2 TB/s with ECC support
Programmable Tensor Cores
Flexible tensor processing units supporting INT4/INT8/FP16/BF16/FP32 with dynamic precision switching for optimal performance.
Hierarchical Memory
Multi-level cache hierarchy with 256KB L1, 32MB L2, and HBM3 main memory delivering 3.2 TB/s bandwidth.
Heterogeneous Compute
Mixed compute clusters combining scalar, vector, and tensor units for diverse AI workload optimization.
High-Speed Interconnect
Custom NeuralLink fabric with 900 GB/s chip-to-chip bandwidth for seamless multi-accelerator scaling.
Hardware Sparsity
Native support for structured and unstructured sparsity achieving up to 4x speedup on sparse models.
Attention Accelerator
Dedicated hardware for self-attention and cross-attention operations with fused kernel support.
Developer SDK
Simple, powerful APIs for Python, C++, and CLI with seamless framework integration
import neuralchip as nc
# Initialize NeuralChip device
device = nc.Device("C7")
# Load and optimize model
model = nc.Model.from_pytorch("resnet50.pth")
optimized = model.optimize(
precision="fp16",
batch_size=64,
enable_sparsity=True
)
# Run inference
input_tensor = nc.Tensor(data, device=device)
output = optimized.infer(input_tensor)
print(f"Latency: {output.latency_ms:.2f}ms")Technical Specifications
Process & Packaging
Process Node
TSMC 5nm FinFET
Die Size
826 mm²
Transistors
80 Billion
Package
CoWoS-S 2.5D
Power & Thermal
TDP
300W
Idle Power
15W
Operating Temp
0-90°C
Cooling
Active + Liquid