AI Accelerator Architecture Co-Design

Turn workload intent into measurable compute, memory, communication and timing decisions

PHASE 2 · AI ACCELERATOR CO-DESIGN

Design compute, memory, communication and clocks together

Explore the architecture principles from the supplied chip-design transcript with explicit calculations. The model compares array organizations, memory policies, pipeline timing and implementation economics; AI then challenges the selected result and prescribes evidence-producing experiments.

Open phase-2 gate

Workload and implementation contract

GEMM shape is M × K multiplied by K × N. All values remain editable assumptions until measured.

Workload

M rows

K reduction

N columns

Batch

Precision

Latency target (ms)

Compute and memory

Array rows

Array columns

Physical arrays

Local SRAM (MiB)

Off-chip bandwidth (GB/s)

Organization

Dataflow

Local-memory policy

Clock, power and product choice

Clock (GHz)

Voltage (V)

Activity factor

Feed-forward logic depth

Feedback-loop depth

Workload change interval (days)

Production volume

Implementation

SELECTED ARCHITECTURE

Splittable hybrid array

Latency target modeled

Compute roof

Deterministic scratchpad

Peak compute

32.768 TOPS

16,384 MACs @ 1 GHz

Sustained roof

30.88 TOPS

94.2% modeled utilization

Workload latency

0.0695 ms

Target ≤ 2 ms

Compute / communication

682.67 ops/B

3 MiB modeled traffic

Array boundary

256 GB/s

4,096 cycles to load weights

Pipeline

2 stages

62 gate levels/stage

Quantization & roofline

The same workload at INT4, INT8 and FP16/BF16 precision. Lower precision raises effective MAC throughput and reduces bytes per element, which moves both roofs and the ridge point.

Precision	Effective peak	Bandwidth roof	Sustained	Bottleneck
INT4	65.536 TOPS	349.525 TOPS	61.735 TOPS	Compute roof
INT8	32.768 TOPS	174.763 TOPS	30.867 TOPS	Compute roof
FP16 / BF16	16.384 TOPS	87.381 TOPS	15.434 TOPS	Compute roof

Roofline region: compute-bound

Ridge point: 128 ops/B

Intensity: 682.67 ops/B

Arithmetic intensity 682.67 ops/B is above the ridge point, so compute and array utilization dominate: ridge point 128 ops/B.

Tile recommendation

4 × 4 tile · 99.4% modeled array utilization

Sampled 900 candidate tiles within 64 × 64 upper bounds; 4 × 4 maximizes modeled array utilization while preferring square tiles. Fill/drain and control overhead beyond the analytical model still require simulation.

Organization comparison

Organization	Utilization	Sustained	Latency	Area index	Power index
Coarse TPU-style array	89%	29.178 TOPS	0.0736 ms	18,186.2	6.82
Fine GPU-style tiles	97.2%	31.835 TOPS	0.0675 ms	20,807.7	7.63
Splittable hybrid array	94.2%	30.88 TOPS	0.0695 ms	19,497	7.16

Area and power are relative comparison indices. They deliberately do not pretend to replace a PDK, SRAM compiler, synthesis, activity-based power analysis or physical implementation.

Memory and communication

Weights fit in local SRAM.

Array-local footprint: 16 KiB. Arithmetic intensity: 682.67 ops/B. Bandwidth roof: 174.763 TOPS.

Clock and recurrence

Feedback loop fits the analytical clock budget.

Cycle: 1,000 ps. Recurrence Fmax: 3.125 GHz.

ASIC versus FPGA

Recommendation: ASIC

Stable workload and production volume favor dedicated gates, lower energy and amortized NRE.

Dynamic-power direction

Relative index: 7.16

Driven by activity × voltage² × frequency × active compute. Validate with workload-derived switching activity.

AI architecture challenge

Send the exact inputs, calculations and three organization alternatives to the configured AI model. The response must identify missing evidence and phase-2 closure experiments; it cannot approve architecture freeze.

Required evidence before architecture freeze

Representative model/operator trace with matrix shapes and batch distribution

SRAM compiler area, timing and power views at the selected process corner

Post-synthesis MAC, pipeline and recurrence timing reports

Measured or simulated off-chip bandwidth efficiency and transfer overlap

Workload-derived activity trace for power estimation

ASIC synthesis, floorplan, clock, power and NRE/volume review

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.

storage

Hierarchical Memory

Multi-level cache hierarchy with 256KB L1, 32MB L2, and HBM3 main memory delivering 3.2 TB/s bandwidth.

account_tree

Heterogeneous Compute

Mixed compute clusters combining scalar, vector, and tensor units for diverse AI workload optimization.

lan

High-Speed Interconnect

Custom NeuralLink fabric with 900 GB/s chip-to-chip bandwidth for seamless multi-accelerator scaling.

settings_suggest

Hardware Sparsity

Native support for structured and unstructured sparsity achieving up to 4x speedup on sparse models.

psychology

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